United States 

■■ Environmental Protection 

■■I R ^Agency 


/ 


TD 884 
.5 

. W35 
2006 
Copy 2 


FT MEADE 
GenCol1 


Estimating Contributions of 
Outdoor Fine Particles to 
Indoor Concentrations and 
Personal Exposures: 

Effects of Household Characteristics 
and Personal Activities 


RESEARCH AND DEVELOPMENT 


USRAfnr of Congress 


TD9M 

7 . 0 ^ 

Cofi 't- 



V 


EPA 600/R-06/023 
March 2006 


Estimating Contributions of Outdoor Fine 
Particles to Indoor Concentrations and 

Personal Exposures: 

Effects of Household Characteristics and 

Personal Activities 


By 


Lance Wallace, Ron Williams, Jack Suggs and Paul Jones 
Human Exposure and Atmospheric Sciences Division 
National Exposure Research Laboratory 
Research Triangle Park, NC, 27711 



National Exposure Research Laboratory 
Office of Research and Development 
U.S. Environmental Protection Agency 
Research Triangle Park, NC, 27711 



Notice 


The U.S. Environmental Protection Agency through its Office of Research and Development partially 
funded and collaborated in the research described here under contract numbers 68-D2-0134 (QST 
Environmental), 68-D2-0187 (SRA Technologies, Inc), 68-D-99-012,68-D5-0040 (Research Triangle Institute), 
CR-820076 (University of North Carolina-Chapel Hill), and CR-828186-01-0 (Shaw University). It has been 
subjected to the Agency's peer and administrative review, and it has been approved for publication as an EPA 
document. Mention of trade names or commercial products does not constitute endorsement or 
recommendation for use. 


\ 


11 


Abstract 


A longitudinal study of personal, indoor, and outdoor exposures to PM: 5 and associated elements was 
carried out involving 37 residents of the Research Triangle Park area in North Carolina. Participant exposures 
were monitored for 7 consecutive days in each of four seasons. A main goal of the study was to estimate the 
contribution of outdoor PM: 5 to indoor concentrations and personal exposures. This contribution depends on 
the infiltration factor (the fraction of outdoor PM: 5 remaining airborne after penetrating indoors), which can be 
estimated using sulfur as a marker for particles of outdoor origin. The annual average infiltration factors ranged 
from 0.26 to 0.89, and depended strongly on air exchange rates. The outdoor contributions to personal exposure 
were then regressed longitudinally on outdoor concentrations measured at a central monitoring station, with a 
range of R" values from 0.19 to 0.88. Variables significantly affecting indoor air PM: 5 concentrations included 
smoking and cooking, the number of persons in the household, burned food, use of a kitchen exhaust fan, and 
duration of candle use. These findings might have important implications for epidemiological studies. 


Contents 


Notice.ii 

Abstract.iii 

Figures. v 

Tables .vii 

Acknowledgments.ix 

Chapter 1: Introduction.1 

Chapter 2: Description of Study Methods and Database.3 

Chapter 3: Results.4 

Calculation of F in f Using the Indoor/Outdoor Sulfur Ratio.4 

Calculation of F in f by Regressing Indoor Sulfur on Outdoor Sulfur. 8 

Comparison of Methods for Calculating Fmf. 11 

Estimating Indoor and Outdoor Contributions to Indoor PM 2.5 . 11 

Relationship Between Outdoor Particles and Indoor Particles of Outdoor Origin. 15 

Estimating Contributions of Outdoor Air to Indoor Concentrations Using the RCS Model. 15 

Estimates of the Contribution of Outdoor Air Particles to Personal Exposure. 17 

Outdoor Exposure Factor F^ Estimated Using PM Measurements.19 

Estimating the Outdoor Exposure Factor F^ Using Sulfur Measurements.19 

Comparison of F pex and F inf .19 

Comparison of Indoor-Outdoor Sulfur and Personal Sulfur Measurements.20 

Use of the Outdoor Exposure Factor to Calculate the Contribution to Personal Exposure Made by 

Particles of Outdoor Ongin.25 

Relationship Between Outdoor Concentrations and the Contnbution to Personal Exposure of Particles of 

Outdoor Origin. 28 

Use of Reported Time in Indoor and Outdoor Microenvironments to Predict the Outdoor Exposure 

Factor F pex from the Infiltration Factor F in f.28 

Estimating P and k.32 

Calculating Average Values of P and k.32 

Calculating Individual Home Values of P and k.33 

Estimating F inf from Individual Values of P and k.36 

Seasonal Analysis.39 

Multivariate Regressions.!.39 

Variables Affecting Air Exchange and the Infiltration Factor.49 

Variables Affecting Air Exchange.49 

Variables Affecting the Infiltration Factor.53 

Chapter 4: Discussion.55 

Chapter 5: Conclusions.58 

Chapter 6 : References.60 

Appendix.64 

\ 


IV 





































OJ U> 


Figures 

Number Page 

3-1 Valid pairs of indoor and outdoor 24-h average sulfur measurements (ng/m 3 ).4 

3-2 Indoor/outdoor sulfur ratios (F in J) by home averaged across all seasons. Error bars are standard 

errors calculated by propagation of error. 8 

3-3 Indoor/outdoor sulfur ratios by home and by season. 8 

3-4 Comparison of the results of regressing indoor sulfur on outdoor sulfur with the simple 

indoor/outdoor ratio averaged over all visits to a home. 11 

3-5 Estimates of the fractional contribution of outdoor particles to total indoor PM; 5 

concentrations, averaged over all home visits. Error bars are standard errors calculated by 

propagation of error. 11 

3-6 Companson of average outdoor-generated and mdoor-generated particles based on 

mdoor/outdoor sulfur ratios. 11 

3-7 Estimates of average outdoor contnbution to indoor PM; 5 . Error bars are standard errors 

calculated by propagation of error techniques applied to the three measurements required to 

estimate the outdoor contribution. 13 

3-8 Estimates of average indoor-generated PM; 5. Error bars are standard errors calculated by 
propagation of error techniques applied to the four measurements required to estimate 

mdoor-generated PM ;. 5 . 13 

3-9 Indoor-outdoor average contributions to indoor PM; 5. Summer 2000. 13 

3-10 Indoor-outdoor average contributions to indoor PM; 5 . Fall 2000 . 13 

-11 Indoor-outdoor average contributions to indoor PM; 5. Winter 2001 . 15 

-12 Indoor-outdoor average contributions to indoor PM; 5. Spring 2001 . 15 

3-13 Regression of indoor PM2.5 on residential outdoor PM 2 5 data. 17 

3-14 Estimates of the infiltration factor /q^from sulfur mdoor/outdoor ratios compared to 
regressions of indoor vs. outdoor fine particles. The regression line shown is for the 

18 cases with slopes significantly different from zero. 17 

3-15 24-h average fine particle personal exposures vs. outdoor air concentrations. 19 

3-16 Personal vs. outdoor PM 15 ; one outlier removed.19 

3-17 Personal vs. outdoor sulfur. 19 

3-18 Co-located PEM 10 and HI; 5 sulfur concentrations outdoors. Summer 2000. 20 

3-19 Co-located PEM 10 and HI; 5 sulfur concentrations indoors. Summer 2000. 20 

3-20 Comparison of infiltration factor F inf and outdoor exposure factor F pex by participant.20 

3-21 Outdoor contributions to personal exposure. Error bars are standard errors calculated by 

propagation of error.25 

3-22 Non-outdoor contributions to personal exposure. Error bars are standard errors 

calculated by propagation of error.25 

3-23 Outdoor and non-outdoor contributions to personal PM 2 5 exposure.25 

3-24 Outdoor and non-outdoor contributions to personal PM; 5 exposure. The non-outdoor 

contnbution is divided into mdoor-generated PM; 5 encountered while at home and the 

sum of mdoor-generated PM; 5 while away from home and PM; 5 due to the personal cloud.28 

3-25 Predicted value of the outdoor exposure factor F pex using Equation 3-6 compared to 

the measured value using the personal/outdoor sulfur ratio.32 

3-26 Predicted personal exposure to PM 2 . 5 using only F mf . .32 

3-27 Nonlinear least-squares fit to the indoor/outdoor suifur ratio vs. the air exchange rate. 

Bounding curves are + 1 SE..32 

3-28 Regression of the outdoor/indoor sulfur ratio vs. residence time.33 

3-29 Same regression as in Figure 3-28 without four outliers.33 

3-30 Companson of estimates of P from the linear and nonlinear approaches described in the text. 

Only values significantly different from zero are plotted (N = 32 homes). 36 
































3-31 Comparison of the estimates of k from the linear and nonlinear approaches described in the text. 

Only values significantly different from zero are plotted (N = 24 homes).36 

3-32 Comparison of the infiltration factor (F m f) estimates from the simple ratio of indoor sulfur to 
outdoor sulfur by home vs. the nonlinear regression of the same ratio using the 
measured air exchange rates and the linear regression of the inverse ratio (outdoor/indoor) 

against the residence time.36 

3-33 Estimates for each home by season of the infiltration factor F,„/-from regressing indoor 
sulfur on outdoor sulfur (Slope) compared to estimates from the simple ratio of indoor 

sulfur to outdoor sulfur averaged over all visits in a season.39 

3-34 Estimates of F m f by home from the mdoor/outdoor sulfur ratio. 39 

3-35 Estimates of F in j by home from regressions of indoor on outdoor sulfur. 39 

3-36 Central-site and residential outdoor concentrations averaged over all visits to a home.40 

A-l Adjusted R“ values from regressing the outdoor contnbution to personal exposure on outdoor 

PM; 5 measurements just outside the house.64 

A-2 Adjusted R“ values from regressing the outdoor contnbution to personal exposure on outdoor 

PM; 5 Harvard Impactor (HI) measurements at the central site.64 

A-3 Adjusted R“ values from regressing the outdoor contribution to personal exposure on outdoor 

PM; 5 Federal Reference Method (FRM) measurements at the central site.65 


vi 











OJ U) UJ U) LtJ 


Tables 


Number Page 

3-1 PM : 5 (jig/m 3 ) and Sulfur Concentrations (ng/m 3 ) Observed in Matched Indoor-Outdoor 

Samples in the RTP Study.5 

3-2 Indoor and Outdoor 24-hour Sulfur Measurements (ng/m 3 ) Averaged Over All Visits 

To Each Home. 6 

3-3 Ratios of the Mean Indoor Sulfur Concentrations to the Mean Outdoor Sulfur Concentrations.7 

3-4 Air Exchange Rates (h' 1 ) and Sulfur Indoor/Outdoor Ratios by Season.9 

3-5 Results of Regressions of Indoor Sulfur on Outdoor Sulfur by Home, Compared to Average 

Values of the Indoor/Outdoor Sulfur Ratio. 10 

3-6 Estimated Annual Average Contributions of Outdoor and Indoor-Generated Particles to 

Total Indoor PM 25 Concentrations by House (pg/m 3 ). 12 

3-7 Estimated Average Contributions of Outdoor Particles and Indoor-Generated Particles to 

Total Indoor Concentrations (pg/m 3 ) by House and by Season. 14 

-8 Regressions of Indoor PM 25 of Outdoor Origin on Outdoor PM 2 5 Measured at the Home. 16 

-9 Regressions of Indoor on Outdoor PM 2 5 by House. 18 

-10 Sulfur Concentrations (ng/m 3 ) and Ratios in Matched Personal, Indoor, and Outdoor Samples.21 

-11 Personal and Indoor Sulfur Concentrations (ng/m 3 ) by Subject.22 

-12 Ratios of the Mean Personal Sulfur Exposure to the Mean Outdoor Sulfur Concentration 

(Fpex) by Subject .23 

3-13 Comparison of Personal/Outdoor, Indoor/Outdoor, and Personal/Indoor Sulfur Ratios by 

House and by Season.24 

3-14 Estimated Contribution of Outdoor Particles to Personal Exposure (pg/m 3 ). Standard 

Deviations and Standard Errors Calculated by Propagation of Error.26 

3-15 Contributions to Personal Exposure from PM 2 5 Particles of Outdoor and Non-Outdoor Origin.27 

3-16 Regression of Personal Exposure to Particles of Outdoor Origin on Outdoor Concentrations 

Measured Near Residence by Harvard Impactor (HI).29 

3-17 Regression of Personal Exposure to Particles of Outdoor Origin on Outdoor Concentrations 

Measured at Central Site by Federal Reference Method (FRM). 30 

3-18 Time (in Minutes) Spent in Various Activities/Locations.31 

3-19 Estimates of P and k for Individual Homes Using Nonlinear Fit to the Indoor/Outdoor 

Sulfur Ratio.34 

3-20 Values for k when P is Bound from Above by 1.35 

3-21 Results of Linear Regressions of the Outdoor/Indoor Sulfur Ratio on Residence Time for 

36 Homes.37 

3-22 Values for k When P is Bound from Above by 1.38 

3-23 Multiple Regression of Outdoor Concentrations on Household Characteristics and Personal 

Activities .41 

3-24 Dependence of Indoor Fine Particle Concentrations on Household Characteristics and Personal 

Activities.43 

3-25 Dependence of Indoor-Generated and Outdoor-Generated Particles on Household Characteristics 

and Personal Activities. 44 

3-26 Dependence of Outdoor Sulfur on Outdoor PM 2 5 and of Indoor Sulfur on Outdoor 

Sulfur and Household Characteristics and Personal Activities.46 

3-27 Regressions of Personal Exposures to PM 2 5 on Household Characteristics and Personal Activities .. 47 


vii 




























3-28 Regression of the Non-ambient-related Contribution ( Perscontrib ) to Personal PM; 5 Exposure.50 

3-29 Regression of the Ambient-Related Contnbution to Personal PM; 5 Exposure.51 

3-30 Regressions of Personal Exposure to Sulfur on Indoor and Outdoor Concentrations and 

Questionnaire Variables. 52 

3-31 Variables Affecting Air Exchange Rate.54 

3-32 Variables Affecting Indoor/Outdoor Sulfur Ratio.54 

3-33 Variables Affecting Indoor/Outdoor Sulfur Ratio: Reduced Model.54 

A-l Values of the Average Sulfur Indoor/Outdoor Ratio (F,„/) and the Air Exchange Rates by 

House and by Season . 66 

A-2 Comparison of Seasonal Average Sulfur Indoor/Outdoor Ratios (S in /S out ) with Slopes of 

Regressions of Sin on Sout.69 

A-3 Comparison of Seasonal Average Sulfur Personal/Outdoor Ratios (Spe rs /S out ) with 

Slopes of Regressions of Spers on Sout.72 

A-4 Questionnaire Variables and Definitions.75 


viii 












Acknowledgments 


The authors thank Linda Sheldon for many stimulating discussions regarding this report. Robert Kellogg 
of Man Tech Environmental performed the X-ray fluorescence analyses that resulted in the excellent sulfur 
data. Charles Rodes led the team at Research Triangle Institute International in collecting the field data. The 
Environmental Measurements and Analysis Branch of the National Exposure Research Laboratory was 
responsible for designing and overseeing the study. In particular, we acknowledge the contributions of Carry 
Croghan, Anne Rea, Alan Vette, Carvin Stevens, Teri Conner, and Kelly Leovic. We wish to especially thank 
the participants who carried the burden of responsibility for up to 28 days over a year’s time. 


IX 











Chapter 1 
Introduction 


Many studies worldwide in the last decade have documented an 
association between health effects and particle concentrations 
measured at central momtonng sites (Schwartz et al., 1996). 
Since the health effects are presumably related to personal 
exposures, an important research need, identified by the National 
Academy of Sciences in 1995, is to determine how personal 
exposures correlate with these outdoor concentrations (NRC- 
NAS 1998). A number of studies (Abt 2000a,b; Allen et al., 
2003; Clayton et al., 1993: Ebelt et al., 2000; Evans et al., 2000; 
Hopke et al., 2003; Howard-Reed et al., 2000; Janssen et al., 
1997, 1998, 1999, 2000; Keeler etal., 2002; Landis et al., 2001; 
Liu et al., 2003; Long et al., 2000, 2001; Ozkaynak et al., 
1996a,b; Pellizzan etal., 1992; Reaet al., 2001; Rojas-Brachoet 
al., 2000; Samat et al., 2000,2001; Thomas et al., 1993; US EPA 
2002, 2003; Vette et al., 2001; Wallace et al., 2003a; Williams et 
al., 2000a,b, 2003a,b) have measured personal exposure directly 
using personal monitors, and the correlations of personal 
exposure with outdoor concentrations are straightforward to 
determine (Wallace, 2000). However, the correlation that 
interests epidemiologists is not that between total personal 
exposure and outdoor concentrations, but the correlation between 
that component of personal exposure due to outdoor particles and 
the outdoor concentrations. This requires the ability to estimate 
the contribution to personal exposures from particles originating 
outdoors. Only a few studies have reported making this estimate 
(Ebelt et al., 2000; Allen et al., 2003, 2004). The goal of this 
report is to estimate the contribution of outdoor particles to 
personal exposure for a group of 37 persons monitored one week 
per season over four seasons in 2000-2001 (US EPA 2002,2003; 
Williams et al., 2003a,b). The correlation between this portion 
of personal exposure and PM: 5 outdoor concentrations will then 
be calculated for each person. Many of the main findings of this 
report appear in Wallace and Williams (2005). 

Since people spend on average 89% of their time indoors, the 
contribution of outdoor particles to indoor concentrations will 
also be explored. For many people, the indoor-outdoor 
relationship may be the major determinant of the personal- 
outdoor relationship. 

The contribution of outdoor particles to indoor concentrations is 
described by the mass balance equation. The full mass balance 
equation includes such phenomena as coagulation, condensation, 
and gas-to-particle conversion (Nazaroff and Cass, 1989). We 
will consider here a simplified version involving only infiltration, 
exfiltration, deposition, and indoor sources. The differential 
form of this simplified mass balance equation is 


dC Jdt = PaC out - (a+k) C,„ + S/V (1 -1) 

where C,„ = indoor number or mass concentration (cm ' 3 or 

pg/m 3 ), 

C ou t = outdoor number or mass concentration 
P = penetration coefficient across building envelope 
a - air exchange rate (h ' 1 ) 

V= volume of building (cm 3 or m 3 ) 

S = source strength (h ' 1 or pg h' 1 ) 
k = deposition rate of particles (h' 1 ) 

The equation is assumed to be applicable to all particle sizes, 
with all terms except air exchange rate and building volume 
considered to be functions of particle size. We assumed that the 
entire house is a single well-mixed zone, with instantaneous 
mixing of particles throughout the house, and that the measured 
air exchange rate in one room applies to the entire house. This 
assumption is probably violated by most homes, which are likely 
to have different zones on each floor or even on the same floor. 
We also assume that the deposition rate is constant over the 
period of integration. This assumption will not hold if persons 
open windows, turn on fans, run the furnace or the air 
conditioner, or otherwise make changes in the household 
operating characteristics that will affect particle deposition 
during the integration period. Finally, we assume that the 
averaging time over which the equation is to be evaluated is 
sufficiently long that transient terms due to short-term changes in 
the outdoor concentration are negligible compared to the long¬ 
term average concentrations. Since we are dealing with 24-hour 
averages, this assumption is probably a good one. Under these 
assumptions, the solution to the mass balance equation is 

C,n = [Pa/(a+k)] * C out + S/[V(a+k)] (1-2) 

The coefficient of the outdoor concentration is sometimes called 
the infiltration factor F m f. 

F mf = Pa/(a+k) (1-3) 

The infiltration factor has a major effect on the mdoor-outdoor 
and the personal-outdoor relationships. This is expected to vary 
by household and resident characteristics. For example, a tightly 
built house may have a lower penetration coefficient than a 
drafty house, although almost no data are available to support 
this claim. A house with a large surface area (e. g., many carpets, 
rugs, or fibrous wall hangings) may have higher deposition rates 
(Lai and Nazaroff 2000). Use of fans or filters may also increase 
particle deposition rates (Howard-Reed et al., 2003; Riley et al.. 


1 



2002; Thatcher et al., 2002; Thatcher and Layton, 1995; Wallace 
et al., 2004a). Open windows will increase F m f by increasing the 
air exchange rate (Howard-Reed et al., 2002; Wallace and 
Howard-Reed, 2002) and possibly by redirecting infiltrating 
particles through the open window (P = 1) rather than through 
the rest of the building envelope (P < 1) (Liu and Nazaroff, 2001; 
Mosley et al., 2001; Thornburg et al., 2001). Use of air 
conditioning has been shown to lower the infiltration factor, 
either because of reducing air exchange rates by shutting 
windows or increasing deposition rates by recirculating indoor 
air through ductwork (Howard-Reed et al., 2003; Samat et al., 
2000; Lai et al., 1999; Thornburg, 2004; Wallace et al., 2002). 

Despite these clear indications that exposure to outdoor air 
particles indoors depends heavily on household and behavioral 
characteristics, studies capable of estimating the infiltration 
factor reliably for individual homes are rather few. Even more 
rare are studies capable of estimating the values of P and k for 
individual homes (Allen et al., 2004). In this study we attempt to 
estimate both F m f and its parameters P and k for individual 
homes, together with an estimate of the uncertainties involved. 

Several investigators have noted that sulfur has few indoor 
sources (Ebelt et al., 2000; Samat et al., 2002). If that is the 
case, the source term in Equation 1 -2 above may be ignored, and 
the equation takes the very simple form 

Sir/Sout — Finf (1-4) 

where S m and S ou , are the sulfur concentrations indoors and 
outdoors. 

Indoor-outdoor comparisons of sulfur concentrations thus 
provide a direct way to estimate F mf for each individual home. 
Strictly speaking, the sulfur data provides only information for 
particles with similar behavior to that of sulfur with respect to 
penetration, deposition, and reactivity. Sulfur particles are 
smaller than most other fine particles. Since the particles in the 
1-2.5 pm range may have higher deposition velocities than 
sulfur, the estimates for the sulfur deposition rate k s would be 
slight underestimates for the typical aerosol mixture in the PM2.5 
category. To avoid extra notation, we shall use P and k 
throughout rather than P s and k s but the reader should remember 
that these are values appropriate only for particles in the size 
range of sulfur particles. Later we provide evidence that this 
“sulfur-related" infiltration factor is actually a very good estimate 
of the PM : 5 infiltration factor. 


V 


9 



Chapter 2 

Description of Study Methods and Database 


A full description of the measurement methods used in this study 
has been provided in the two preceding reports in this series (US 
EPA 2002,2003) and also in two published articles (Williams et 
al., 2003a,b). The following abbreviated description includes 
only the methods discussed in this report. 

In this study, 37 persons in 36 homes in the Research Triangle 
Park, NC area were monitored for up to four seasons, 7 days per 
season. (Thirteen of these were monitored for only one, two, or 
three seasons; see Appendix A.) Two gravimetric monitor types 
were employed for PM 2 5 measurements: the Harvard Impactor 
(HI), operating at 20 Lpm, and a Personal Exposure Monitor 
(PEM), operating at 2 Lpm for a nominal 24-h period. The PEM 
was used for personal samples and the HI for indoor-outdoor 
samples, the latter measured just outside the home. An HI, a 
PEM and a Federal Reference Method (FRM) monitor were also 
operated every day of the study at a central site. There were also 
fixed PEM io monitors co-located with the HI? 5 monitors—filters 
from these instruments were analyzed for a suite of elements 
using x-ray fluorescence. Since sulfur particles are expected to 
be in the size range <0.5 pm, the PEM 10 filters should have the 
same amount of sulfur as the PEM? 5 filters.) 

The HI mass measurements had a precision of about 5%; the 
PEM measurements had a precision of about 8% (Williams et al., 
2003a). The precision of the sulfur measurements was calculated 
to be about 8% (Kellogg, R, personal communication). 

The data analysis concentrated on sulfur as a tracer of outdoor¬ 
generated particles. Indoor-outdoor ratios were used to estimate 
the infiltration factor for PM 25 ; personal-outdoor ratios were 
used to estimate the portion of personal exposure due to outdoor¬ 
generated particles. The estimates of exposure due to outdoor¬ 
generated particles were then regressed on outdoor 
measurements, both at the home and at a central site, to 
determine the relationship between central site measurements 
and exposure to particles of outdoor origin. 


Participants filled out activity logs each day, identifying cooking, 
cleaning, and other activities that might affect particle exposures. 
A questionnaire on household characteristics was also completed 
for each house. The full set of questionnaire variables and their 
definitions and units is provided in Appendix A (Table A-4). 
These variables were then included in multivariate regressions to 
determine their influence on air exchange rates, the infiltration 
factor, and the observed PM 2 5 and sulfur concentrations. A 
detailed description of this analysis is provided in the section on 
multivariate regressions. 


3 



Chapter 3 
Results 


A total of 876 person-days had at least one personal, indoor, or 
outdoor PM 2 .5 measurement. About 868 days had at least one 
filter analyzed by XRF. Samples were flagged if they failed any 
of a number of quality control criteria. For example, flow rates 
were required to be within 10 % of the target values, filters were 
discarded if seen to be tom or pierced. All outdoor PM 2.5 filters 
collected between April 11 and April 17 were discarded due to 
excessive contamination with pollen. An additional requirement 
for the sulfur measurements was that indoor values not be more 
than 1.08 times outdoor values (the 1.08 factor was chosen 
because the estimated uncertainty of the sulfur measurements 
was 8 %). Even if an indoor/outdoor ratio exceeding 1.08 were 
correct, it would indicate an indoor source of sulfur, and 
therefore should not be used in analyses to determine the 
infiltration factor. Six samples were flagged for exceeding a 
1.08 indoor/outdoor ratio. Another 21 samples were flagged 
when regressions suggested an indoor source of sulfur, and it 
was discovered that the participant was using a humidifier during 
three seasons (7 days per season). Since different combinations 
of variables will have different numbers of flags, the number of 
valid data points sometimes varies in the following tables. 

Calculation of F inf Using the Indoor/Outdoor 
Sulfur Ratio 

Table 3-1 lists the distributional characteristics of all paired 
indoor-outdoor samples with validated PM 25 and sulfur 
measurements. Also included in Table 3-1 are estimates of the 
indoor/outdoor sulfur ratio. The average indoor/outdoor sulfur 
ratio was 0.59 (0.16 SD). 

Assuming all the sulfur is in the form of ammonium sulfate, we 
can calculate the mass by multiplying by the ratio of the 
molecular weights (3.5). The result is an estimate of 8.0 pg/nf 
of ammonium sulfate, or about 41% of the total PM 2 . 5 . Several 
Eastern cities included in EPA’s speciation network ranged from 
26-31% in their sulfate/PM? 5 values (AQCD 2003). 


All the validated indoor and outdoor sulfur concentrations (N = 
775) are displayed in Figure 3-1. 



Figure 3-1. Valid pairs of indoor and outdoor 24-h average sulfur 
measurements (ng/m 3 ). 

The indoor and outdoor sulfur measurements averaged over all 
visits to each home are displayed in Table 3-2, together with 
their standard deviations and standard errors. Home 29 had two 
participants, 29 and 35. It was visited twice in the first season 
but only once thereafter. Thus it had a maximum of 35 visits 
compared to 28 for most of the other homes. Several homes 
were monitored for 8 days instead of 7 in the first season so that 
their maximum number of visits was 29. 

The ratios of the mean indoor sulfur concentration to the mean 
outdoor sulfur concentration averaged over all visits are shown 
by house in Table 3-3. Since the ratio involves two 
measurements, each with associated error, the standard deviation 
and standard error of the ratio are calculated by propagating the 
proportional errors: 

SD calc = [(a in /ju in ) : + (<J ou /ju 0 J 2 ]' : * HiyJu ou , (3-1) 


4 









Table 3-1. PM 25 (|jg/m 3 ) and Sulfur Concentrations (ng/m 3 ) Observed in Matched Indoor-Outdoor Samples in the RTP Study 



N 

Mean 

SD 

Min 

10 th 

25th 

Median 

75th 

90th 

Max 

PM 2 5 ln 

774 

19.4 

16 

2 

7 

10 

15 

22 

36 

119 

PM 2 5 Out 

774 

19.5 

9 

5 

9 

13 

19 

24 

32 

52 

Sulfur In 

775 

1116 

653 

123 

426 

615 

974 

1441 

2034 

3852 

Sulfur Out 

775 

1964 

1123 

404 

765 

1061 

1759 

2667 

3610 

5406 

S In/Out 

775 

0.59 

0.16 

0.17 

0.39 

0.48 

0.59 

0.69 

0.79 

1.06 


5 






Table 3-2. Indoor and Outdoor 24-hour Sulfur Measurements (ng/m 3 ) Averaged Over All Visits to Each Home 


House 

N 

Sin 

SD 

SE 

S 0 ut 

SD 

SE 

1 

27 

955 

372 

72 

1532 

612 

118 

2 

26 

1069 

477 

94 

1877 

1030 

202 

3 

29 

1516 

534 

99 

2078 

756 

140 

4 

23 

882 

359 

75 

2106 

1056 

220 

5 

27 

1199 

723 

139 

1892 

1034 

199 

6 

28 

1293 

651 

123 

2116 

1008 

191 

7 

28 

1476 

901 

170 

1947 

1261 

238 

8 

6 

1270 

408 

167 

3664 

1376 

562 

9 

24 

1087 

688 

141 

1968 

1310 

267 

10 

27 

1049 

558 

107 

1945 

1202 

231 

11 

8 

1661 

825 

292 

2446 

1266 

447 

12 

28 

944 

449 

85 

1945 

1149 

217 

13 

7 

553 

224 

85 

1534 

661 

250 

14 

27 

701 

342 

66 

1579 

774 

149 

15 

27 

928 

449 

86 

1322 

588 

113 

16 

28 

1040 

554 

105 

1639 

952 

180 

17 

26 

923 

467 

92 

1889 

1140 

224 

18 

6 

1328 

436 

178 

2625 

996 

407 

19 

27 

1568 

769 

148 

2198 

1218 

234 

20 

27 

1139 

641 

123 

2272 

1229 

237 

21 

27 

1240 

704 

135 

2152 

1322 

254 

22 

13 

1231 

607 

168 

2338 

1175 

326 

23 

13 

1175 

601 

167 

2558 

1508 

418 

24 

27 

909 

498 

96 

1795 

1153 

222 

25 

8 

1827 

679 

240 

2712 

1292 

457 

26 

24 

1829 

1009 

206 

2620 

1457 

297 

27 

28 

1335 

704 

133 

2113 

1276 

241 

28 

25 

1059 

502 

100 

1938 

1147 

229 

29 

34 

991 

446 

76 

2001 

1269 

218 

31 

14 

1678 

722 

193 

1920 

839 

224 

32 

28 

607 

287 

54 

1730 

769 

145 

33 

23 

522 

322 

67 

1493 

978 

204 

34 

12 

1746 

808 

233 

2811 

1139 

329 

36 

6 

658 

300 

123 

2525 

1130 

461 

37 

20 

1079 

327 

73 

1836 

698 

156 

38 

17 

534 

137 

33 

974 

299 

72 

Sum/Mean 

775 

1139 

541 

126 

2058 

1058 

252 


X' 


6 







Table 3-3. Ratios of the Mean Indoor Sulfur Concentrations to the Mean Outdoor Sulfur Concentrations 


House 

Sm/Sout 

S D ca i c 

SE calc b 

1 

0.62 

0.35 

0.07 

2 

0.57 

0.40 

0.08 

3 

0.73 

0.37 

0.07 

4 

0.42 

0.27 

0.06 

5 

0.63 

0.52 

0.10 

6 

0.61 

0.42 

0.08 

7 

0.76 

0.67 

0.13 

8 

0.35 

0.17 

0.07 

9 

0.55 

0.51 

0.10 

10 

0.54 

0.44 

0.08 

11 

0.68 

0.49 

0.17 

12 

0.49 

0.37 

0.07 

13 

0.36 

0.21 

0.08 

14 

0.44 

0.31 

0.06 

15 

0.70 

0.46 

0.09 

16 

0.63 

0.50 

0.09 

17 

0.49 

0.38 

0.08 

18 

0.51 

0.25 

0.10 

19 

0.71 

0.53 

0.10 

20 

0.50 

0.39 

0.08 

21 

0.58 

0.48 

0.09 

22 

0.53 

0.37 

0.10 

23 

0.46 

0.36 

0.10 

24 

0.51 

0.43 

0.08 

25 

0.67 

0.41 

0.14 

26 

0.70 

0.55 

0.11 

27 

0.63 

0.51 

0.10 

28 

0.55 

0.41 

0.08 

29 

0.50 

0.38 

0.07 

31 

0.87 

0.54 

0.14 

32 

0.35 

0.23 

0.04 

33 

0.35 

0.31 

0.07 

34 

0.62 

0.38 

0.11 

36 

0.26 

0.17 

0.07 

37 

0.59 

0.29 

0.06 

38 

0.55 

0.22 

0.05 

Mean 

0.56 a 

0.39 

0.09 


a This value obtained by dividing the mean indoor sulfur concentration for all homes by the mean outdoor sulfur concentration. The arithmetic mean 
of the ratios was 0.59. 

b Values of the standard deviation and standard error are calculated by propagation of error. 


7 






where SD ca/c = the calculated standard deviation of the ratio for 
agiven house, 

cr,„ = the standard deviation of the indoor sulfur measurements, 
jUj„ = the mean of the indoor sulfur measurements, 

<j ou , = the standard deviation of the outdoor sulfur measurements, 
ju (H ,i = the mean of the outdoor sulfur measurements 

The calculated standard error is SD catc /N ] 2 . 

The range of indoor/outdoor sulfur ratios averaged over all visits 
to a home is shown in Figure 3-2. Although the interquartile 
range of 0.49 to 0.66 stays within 20% of the ratio of the mean 
indoor to outdoor sulfur concentrations of 0.56, the complete 
range spans a greater than threefold variation, from 0.26 to 0.87. 
The lowest values are homes that participated only in the 
summer season, when apparently air conditioning caused lower 
infiltration generally across most homes. 



Figure 3-2. Indoor/outdoor sulfur ratios (F inf ) by home averaged 
across all seasons. Error bars are standard errors calculated by 
propagation of error. 

Figure 3-2 shows the indoor/outdoor sulfur ratios as they were 
averaged over all seasons. However, the ratios should vary as a 
function of air exchange rate, and the air exchange rate varies by 
season. The highest air exchange rates were in the winter (1.07 
h' 1 ) and the lowest in the summer (0.48 h" 1 ) (Table 3-4), a 
reversal of the normal pattern in many northern U.S. cities (Abt 
et al., 2000b; Ebelt et al, 2000; Long et al., 2001), where 
windows are opened more often in summer. The lower 
indoor/outdoor ratios in the summer (average 0.50) are 
presumably due to the use of air conditioning systems, which 
reduce air exchange rates by requiring closed windows and 
reduce sulfur concentrations by increasing the residence time in 
the house as well as forcing recirculation of indoor air through 
ductwork and filters. On the other hand, the higher air exchange 
rates in winter were not accompanied by comparably higher 
indoor/outdoor sulfur ratios, which were virtually identical (0.62- 
0.63) for all three seasons. 


A plot of the indoor/outdoor sulfur ratios by season shows that 
the seasonal difference in the summer sulfur ratios was shared by 
nearly all of the homes (Figure 3-3). 

1 -i 



Figure 3-3. Indoor/outdoor sulfur ratios by home and by season. 

Calculation of F inf by Regressing Indoor 
Sulfur on Outdoor Sulfur 

A second way to calculate F in f is to regress indoor sulfur 
concentrations on outdoor values. Although this calculation is 
obviously related to the simple calculation of the mean 
indoor/outdoor ratio, the value of the intercept adds to our 
understanding. That is, a nonzero intercept might indicate 
possible indoor sources. In fact, the intercept for home 18 was 
very high (about 1000 ng/nr 3 ) and investigation revealed that this 
subject was using a humidifier in his home for three of the four 
seasons. It is suspected that the subject was using tap water in 
the humidifier and that the dissolved solids in the tap water 
included sulfates or sulfides (Highsmith et al., 1988). Therefore 
the indoor sulfur values for the three seasons not including 
summer (21 person-days) were not used in this analysis. 

Table 3-5 shows generally high R : values for each individual 
home: 14 values of 0.90 or higher and only one value below 
0.50. The slope of the regression was significantly different 
from zero (p<0.05) for every home. (The p-values for the slopes 
are suppressed in the table to save space.) 22 of 36 homes had 
intercepts not significantly different from zero, indicating no 
apparent source of sulfur in the home. The intercepts that are 
different from zero (shown in boldface in the intercept column) 
tend to be relatively small compared to the average indoor S 
value of > 100 ng/m' and thus may be due to scatter rather than to 
indoor sources of sulfur or sulfates. The scatter is due both to 
variations in F,„/with changing seasons and air exchange rates 


8 


















Table 3-4. Air Exchange Rates (h' 1 ) and Sulfur Indoor/Outdoor Ratios by Season 


Season 

N 

airex 

SD 

Sm/Sout 

SD a 

Summer 

223 

0.49 

0.57 

0.50 

0.16 

Fall 

187 

0.61 

0.40 

0.63 

0.14 

Winter 

179 

1.01 

0.73 

0.63 

0.13 

Spring 

171 

0.68 

0.49 

0.62 

0.16 


Observed standard deviation; SD calculated by propagation of error would be larger. 


9 









Table 3-5. Results of Regressions of Indoor Sulfur on Outdoor Sulfur by Home, Compared to Average Values of the Indoor/Outdoor Sulfur Ratio 


House 

N 

Sm/Sout 

SE 

Slope 

SE 

Inter. 

SE 

P 

R 2 

1 

27 

0.63 

0.02 

0.57 

0.04 

86 

72 

0.24 

0.86 

2 

26 

0.60 

0.02 

0.45 

0.02 

231 

53 

0.00 

0.93 

3 

29 

0.74 

0.02 

0.68 

0.04 

103 

81 

0.21 

0.93 

4 

23 

0.46 

0.02 

0.28 

0.04 

287 

97 

0.01 

0.67 

5 

27 

0.65 

0.02 

0.60 

0.07 

70 

156 

0.66 

0.72 

6 

28 

0.61 

0.02 

0.63 

0.03 

-46 

60 

0.45 

0.96 

7 

28 

0.77 

0.02 

0.69 

0.04 

132 

83 

0.12 

0.93 

8 

6 

0.36 

0.05 

0.27 

0.06 

291 

249 

0.31 

0.77 

9 

24 

0.58 

0.02 

0.49 

0.04 

115 

90 

0.21 

0.88 

10 

27 

0.57 

0.02 

0.42 

0.04 

227 

88 

0.02 

0.82 

11 

8 

0.68 

0.04 

0.63 

0.07 

116 

180 

0.54 

0.93 

12 

28 

0.54 

0.02 

0.36 

0.03 

238 

63 

0.00 

0.86 

13 

7 

0.37 

0.04 

0.32 

0.05 

60 

78 

0.48 

0.88 

14 

27 

0.45 

0.02 

0.41 

0.03 

55 

59 

0.36 

0.85 

15 

27 

0.69 

0.02 

0.72 

0.05 

-23 

74 

0.76 

0.88 

16 

28 

0.65 

0.02 

0.57 

0.03 

111 

50 

0.04 

0.94 

17 

26 

0.52 

0.02 

0.36 

0.04 

238 

85 

0.01 

0.78 

18 

6 

0.52 

0.05 

0.42 

0.06 

226 

173 

0.26 

0.90 

19 

27 

0.75 

0.02 

0.60 

0.04 

239 

91 

0.01 

0.91 

20 

27 

0.49 

0.02 

0.50 

0.03 

-8 

66 

0.90 

0.94 

21 

27 

0.61 

0.02 

0.47 

0.05 

232 

127 

0.08 

0.77 

22 

13 

0.55 

0.03 

0.44 

0.08 

211 

217 

0.35 

0.69 

23 

13 

0.49 

0.03 

0.39 

0.03 

182 

79 

0.04 

0.95 

24 

27 

0.55 

0.02 

0.40 

0.03 

190 

69 

0.01 

0.85 

25 

8 

0.72 

0.04 

0.38 

0.15 

793 

438 

0.12 

0.45 

26 

24 

0.70 

0.02 

0.67 

0.04 

83 

121 

0.50 

0.92 

27 

28 

0.66 

0.02 

0.53 

0.03 

215 

74 

0.01 

0.92 

28 

25 

0.59 

0.02 

0.38 

0.04 

315 

99 

0.00 

0.76 

29 

34 

0.57 

0.02 

0.30 

0.03 

398 

79 

0.00 

0.70 

31 

14 

0.89 

0.03 

0.84 

0.06 

74 

124 

0.56 

0.94 

32 

28 

0.36 

0.02 

0.30 

0.04 

90 

83 

0.28 

0.63 

33 

23 

0.39 

0.02 

0.27 

0.04 

119 

73 

0.12 

0.66 

34 

12 

0.62 

0.03 

0.64 

0.09 

-65 

283 

0.82 

0.81 

36 

6 

0.26 

0.05 

0.26 

0.03 

10 

94 

0.92 

0.92 

37 

20 

0.60 

0.02 

0.41 

0.05 

326 

104 

0.01 

0.75 

38 

17 

0.56 

0.03 

0.40 

0.06 

140 

57 

0.03 

0.76 




10 








and measurement error. These have the well-known effect of 
lower slopes and higher intercepts than the true values. 
Therefore the calculated slopes are likely to be underestimates of 
the true infiltration factor. Only 4 of the 36 homes had slopes 
higher than the mean indoor/outdoor sulfur ratio. The overall 
average indoor/outdoor sulfur ratio is 0.56, compared to the 
overall average slope of 0.49. 

Comparison of Methods for Calculating F inf 

The methods for calculating F in f (regression vs. the simple 
indoor/outdoor ratio) are compared in Figure 3-4. The 
comparison differentiates between the 22 homes with intercepts 
not different from zero and the 14 homes with intercepts 
significantly different from zero. The latter set of homes cannot 
be said with complete confidence to have no indoor sources of 
sulfur, although random measurement errors could also cause 
nonzero intercepts. The slopes for these homes are generally 
lower, as would be expected if measurement errors are affecting 
the regressions. Home number 25 is an outlier in this graph 
(large red diamond), having the highest intercept of all homes, 
but with high uncertainty, due to a small number of samples (N = 
8 ). Without home 25, the 21 remaining homes with intercepts 
not different from zero have slopes that are related to the mean 
indoor/outdoor ratios with a high R' of 0.91. 



Figure 3-4. Comparison of the results of regressing indoor sulfur on 
outdoor sulfur with the simple indoor/outdoor ratio averaged over all 
visits to a home. 

Estimating Indoor and Outdoor 
Contributions to Indoor PM2.5 

The indoor/outdoor sulfur ratio may be multiplied by the outdoor 
air concentration to estimate the contribution of outdoor air 
particles to the total indoor particle level for each home (Table 3- 
6 ). The indoor-generated particles are then the difference 
between the total indoor PM 2 5 and the outdoor contribution to 
indoor PM2.5. The fractional contributions of outdoor air particles 
to indoor concentrations, averaged over all visits to each home, 
are shown in Figure 3-5. Fourteen homes had more than 80% of 


their indoor PM 2 5 concentrations supplied by outdoor air, and 7 
homes had less than 40% supplied by outdoor air. 



House ID 


Figure 3-5. Estimates of the fractional contribution of outdoor 
particles to total indoor PM 2 5 concentrations, averaged over all home 
visits. Error bars are standard errors calculated by propagation of 
error. 

The outdoor and indoor-generated contributions to total indoor 
PM 2 . 5 , averaged over all visits to each house, are shown in Figure 
3-6. The relative importance of these two sources varies widely, 
with some homes having essentially no indoor contribution and 
others having more than 50% indoor contributions. 



House ID 

Figure 3-6. Comparison of average outdoor-generated and indoor¬ 
generated particles based on indoor/outdoor sulfur ratios. 

The 95% confidence limits are displayed separately for outdoor 
and indoor contributions in Figures 3-7 and 3-8. (These values 
are taken from Table 3-6.) The indoor contributions cover a 
wider range than the outdoor contributions. For example, three 
homes had indoor sources producing an average concentration 
over the four seasons > 25 pg/nr, compared to no homes with 
outdoor contributions that high. On the other hand, 20 homes 
had indoor contributions of 5 pg/nr or lower compared to only 


11 






























































Table 3-6. Estimated Annual Average Contributions of Outdoor and Indoor-Generated Particles to Total Indoor PM 2 5 Concentrations by House (pg/m 3 ) 


House 

N 

Outdoor contribution 
to indoor particles 

SDcalc 

SE C ai c 

Indoor-generated 

particles 

SD ca | C 

SE C alc 

1 

27 

11.0 

12.9 

2.5 

5.0 

15.9 

3.1 

2 

26 

13.8 

18.8 

3.7 

2.3 

21.3 

4.2 

3 

29 

19.4 

16.6 

3.1 

1.6 

20.0 

3.7 

4 

23 

9.4 

16.8 

3.5 

31.0 

37.0 

7.7 

5 

27 

12.2 

17.4 

3.4 

5.9 

20.9 

4.0 

6 

28 

11.7 

15.0 

2.8 

5.8 

16.5 

3.1 

7 

28 

18.0 

22.6 

4.3 

1.6 

25.5 

4.8 

8 

6 

9.6 

15.8 

6.5 

8.1 

16.5 

6.7 

9 

24 

11.3 

20.0 

4.1 

4.8 

22.0 

4.5 

10 

27 

10.9 

18.9 

3.6 

0.2 

19.3 

3.7 

11 

8 

15.5 

17.8 

6.3 

11.8 

22.2 

7.8 

12 

28 

9.1 

16.1 

3.1 

3.7 

16.9 

3.2 

13 

7 

4.8 

9.6 

3.6 

3.8 

10.4 

3.9 

14 

27 

7.4 

13.3 

2.6 

4.1 

14.8 

2.8 

15 

27 

10.8 

11.4 

2.2 

7.2 

20.3 

3.9 

16 

28 

10.7 

15.1 

2.9 

-0.5 

15.6 

3.0 

17 

26 

8.7 

15.4 

3.0 

4.1 

18.6 

3.6 

18 

6 

10.7 

11.9 

4.8 

1.4 

12.2 

5.0 

19 

27 

17.2 

20.4 

3.9 

3.3 

21.5 

4.1 

20 

27 

10.8 

19.8 

3.8 

-0.3 

20.2 

3.9 

21 

27 

13.0 

20.6 

4.0 

25.4 

29.6 

5.7 

22 

13 

10.6 

15.6 

4.3 

14.0 

23.5 

6.5 

23 

13 

9.9 

19.0 

5.3 

15.1 

33.7 

9.4 

24 

27 

9.4 

16.6 

3.2 

27.6 

29.1 

5.6 

25 

8 

21.4 

19.1 

6.7 

32.6 

29.0 

10.3 

26 

24 

17.6 

22.3 

4.5 

2.2 

24.2 

4.9 

27 

28 

12.7 

18.1 

3.4 

22.7 

32.1 

6.1 

28 

• 

25 

10.4 

15.8 

3.2 

1.0 

16.6 

3.3 

29 

34 

9.7 

16.8 

2.9 

16.3 

22.7 

3.9 

31 

14 

21.7 

20.7 

5.5 

-0.2 

23.2 

6.2 

32 

28 

5.8 

12.3 

2.3 

7.3 

13.6 

2.6 

33 

23 

6.4 

18.0 

3.7 

5.1 

20.0 

4.2 

34 

12 

15.4 

16.7 

4.8 

1.3 

17.3 

5.0 

36 

6 

6.2 

17.4 

7.1 

3.0 

17.5 

7.2 

37 

20 

10.4 

10.4 

2.3 

1.3 

10.9 

2.4 

38 

17 

7.4 

8.3 

2.0 

0.3 

8.8 

2.1 

Sum/ Mean 

775 

11.7 

16.5 

3.9 

7.8 

20.5 

4.8 


12 







one home with an outdoor contribution that low. This reflects 
much more variability in indoor particle-generating activities 
compared with outdoor particle concentration variability. 



Figure 3-7. Estimates of average outdoor contribution to indoor PM 2 5 . 
Error bars are standard errors calculated by propagation of error 
techniques applied to the three measurements required to estimate the 
outdoor contribution. 



Figure 3-8. Estimates of average indoor-generated PM 2 5 . Error bars 
are standard errors calculated by propagation of error techniques 
applied to the four measurements required to estimate indoor¬ 
generated PM 2 5 . 

We saw in Figure 3-3 that the indoor/outdoor sulfur ratio varied 
by season, and was much lower in summer than the other three 
seasons. We next look at the estimates of indoor and outdoor 
contributions by season (Table 3-7; Figures 3-9 through 3-12). 
Table 3-7 shows that indoor-generated particle concentrations 
were 45-46% of the total indoor particle concentrations averaged 
across all homes in the summer and fall seasons, but only 31% in 
the winter and spring seasons. This was due mostly to a 
reduction in indoor sources in the latter two seasons, since the 
outdoor contribution did not change greatly over the four 
seasons. Because of the small number of measurements in each 
house and each season (N = 1-7), the uncertainty, particularly in 


the estimates of indoor-generated concentrations, is much larger 
than for the equivalent concentrations for each home over all 
seasons (as in Table 3-6). The average standard error was 15% 
for the estimates of the outdoor contributions, but 66% for the 
indoor estimates. 



House ID 


Figure 3-9. Indoor-outdoor average contributions to indoor PM 25 . 
Summer 2000. 



House ID 


Figure 3-10. Indoor-outdoor average contributions to indoor PM 25 . 
Fall 2000. 


13 


















































































































































Table 3-7. Estimated Average Contributions of Outdoor Particles and Indoor-Generated Particles to Total Indoor Concentrations (pg/m 3 ) by House 
and by Season 




Summer 2000 


Fall 2000 



Winter 2001 



Spring 2001 


House 

N 

Out 

In 

N 

Out 

In 

N 

Out 

In 

N 

Out 

In 

1 

8 

14.0 

1.6 

6 

8.1 

8.1 

6 

10.8 

12.5 

2 

8.7 

0.7 

2 

6 

14.2 

0.5 

4 

14.9 

-1.8 

6 

14.3 

-1.9 

7 

12.0 

11.3 

3 

7 

16.2 

-0.1 

6 

24.3 

11.8 

7 

23.6 

-6.8 

7 

12.9 

2.0 

4 

2 

7.5 

10.9 

7 

13.5 

44.8 

7 

8.0 

43.9 

4 

6.9 

11.7 

5 

6 

12.0 

16.5 

6 

8.3 

8.7 

7 

16.6 

-1.2 




6 

7 

12.9 

8.5 

5 

18.7 

3.5 

7 

9.3 

8.2 

7 

7.8 

3.2 

7 

7 

17.3 

5.6 

7 

26.0 

3.1 

6 

15.0 

-0.1 

5 

14.7 

-1.8 

8 

6 

9.6 

8.1 










9 

6 

12.4 

3.5 

5 

6.2 

11.8 

5 

15.6 

8.8 

5 

12.0 

-1.3 

10 

6 

10.7 

1.8 

7 

7.5 

1.1 

7 

17.3 

-1.4 

5 

7.5 

0.2 

11 

6 

17.0 

15.4 







2 

11.2 

0.9 

12 

7 

10.0 

5.7 

7 

6.7 

8.3 

7 

10.4 

1.5 

7 

9.4 

-0.7 

13 

7 

4.8 

3.8 










14 

7 

5.0 

4.9 

6 

6.1 

2.7 

7 

10.4 

7.7 

7 

7.7 

0.9 

15 

6 

8.7 

1.5 

7 

9.8 

18.5 

7 

13.1 

1.4 

3 

12.6 

3.4 

16 

6 

13.0 

-0.4 

7 

7.6 

1.3 

7 

11.4 

-1.7 

7 

10.9 

-1.0 

17 

7 

9.4 

5.6 

5 

8.9 

0.6 

6 

9.7 

1.2 

1 

2.8 

2.5 

18 

6 

10.7 

1.4 










19 

6 

16.1 

8.4 

7 

12.3 

5.3 

7 

25.2 

-2.4 

7 

15.2 

2.8 

20 

7 

13.9 

1.0 

6 

5.0 

2.6 

7 

16.0 

-4.9 

7 

7.5 

0.5 

21 

7 

12.1 

25.8 

6 

17.7 

28.1 

7 

13.9 

18.6 

6 

7.7 

19.5 

22 

6 

9.2 

15.3 

7 

11.7 

13.0 







23 

7 

11.4 

5.1 

6 

8.1 

26.9 







24 

7 

8.6 

31.4 

7 

10.7 

23.1 

6 

11.9 

27.9 

7 

6.4 

28.1 

25 

2 

14.4 

51.5 

6 

23.8 

26.2 







26 

6 

18.6 

4.1 

6 

23.3 

4.2 

5 

21.5 

-2.4 

5 

9.2 

4.1 

27 

6 

14.2 

49.9 

7 

9.8 

27.0 

7 

15.2 

12.0 

7 

11.0 

3.9 

28 

5 

10.5 

0.0 

7 

11.7 

1.8 

6 

11.9 

3.3 

6 

8.3 

-0.9 

29 

13 

9.8 

23.0 

6 

7.2 

11.3 

7 

12.9 

18.1 

5 

8.9 

9.7 

31 

7 

13.9 

0.8 

7 

29.5 

-1.2 







32 

7 

3.3 

6.1 

7 

6.9 

10.7 

7 

6.7 

6.4 

7 

6.4 

6.1 

33 

5 

2.6 

2.7 

5 

4.0 

4.3 

6 

10.1 

3.0 

4 

9.1 

4.2 

34 

7 

13.1 

1.6 

3 

21.1 

-0.7 







36 

5 

6.9 

2.7 










37 




5 

8.7 

3.7 

7 

12.2 

0.7 

7 

10.5 

0.3 

38 




3 

4.9 

1.9 

7 

8.0 

0.5 

5 

7.4 

-0.1 

Sum/Mean 

216 

11.3 

9.5 

186 

12.4 

10.0 

171 

13.5 

5.9 

142 

9.4 

4.2 




14 









House ID 


Figure 3-11. Indoor-outdoor average contributions to indoor PM 25 . 
Winter 2001. 



House ID 

Figure 3-12. Indoor-outdoor average contributions to indoor PM 2 5 . 
Spring 2001. 

Relationship Between Outdoor Particles 
and Indoor Particles of Outdoor Origin 

As described in the Introduction, a quantity of interest to 
epidemiologists is the relationship between outdoor particles and 
indoor particles of outdoor origin. Since epidemiologists work 
with measurements made at a central site, we examined how this 
relationship changes as we go from the particles measured just 
outside the home to those measured at the central site, and as we 
go from particles measured by the same type of equipment to 
those measured by different monitors. We first performed 
regressions for each home of the estimated contributions to 
indoor PM 25 made by particles of outdoor origin vs. the 
measured concentrations just outside the home (Table 3-8). 
These individual regressions by home have high R~ values in 
most cases (median = 0.77; range 0.39-0.95). For all 752 valid 
daily measurements, the regression has a slope of 0.56 (0.01 SE) 
and a non-significant intercept of 0.31 (0.31) (Table 3-8). The 


Pearson correlation coefficient was 0.82, with an R : (adjusted) 
value of 0.665. (A Spearman rank correlation was also 0.82, 
indicating that the Pearson coefficient was not affected by 
outliers.) When the regression was run against the HI at the 
central site, the R" value was reduced to 0.58 (N = 736). Against 
the FRM at the central site, it was reduced to 0.49 (N = 775). 
We caution that these R" values must be considered upper 
bounds, because the assumptions of our model, which force the 
infiltrated particles of outdoor origin to be proportional to 
outdoor concentrations, virtually ensure that R~ values will be 
high. 

Estimating Contributions of Outdoor Air to 
Indoor Concentrations Using the RCS Model 

Another way of estimating an average infiltration factor, this 
time using the PM 25 mass data, is the Random Component 
Superposition (RCS) model developed by Ott and co-workers 
(Ott et al., 2000). The RCS model simply assumes that a 
regression of all the measured indoor vs. outdoor mass 
concentrations will provide an overall average infiltration factor 
together with an estimate of the indoor source strength averaged 
across all homes (that being the constant term in the regression). 
The overall average infiltration factor estimated by this approach 
is 0.60 (0.06 SE, N = 774) (Figure 3-13), compared with the 
average indoor/outdoor sulfur ratio of 0.589 (0.006 SE, N = 775) 
from Table 3-1. The close agreement of these two values for 
infiltration factor, one based on particle concentrations alone and 
the other based on sulfur concentrations alone, is important 
evidence that using the sulfur results to estimate fine particle 
parameters is justified, at least for these overall averages. It 
should be noted that the RCS model determines only one average 
value of F in f for all homes, whereas the methods discussed above 
provide estimates for individual homes. The RCS approach as 
applied to PM 10 values in Riverside, CA, Philipsburg, NJ, and 
Toronto, Canada found similar values of 0.55, 0.60 and 0.61 for 
the infiltration factor (Ott et al., 2000) compared to our values 
using PM 2 5 sulfur and mass data. Further evidence for the 
applicability of the sulfur results to the PM? 5 fraction is the close 
agreement between the RCS estimate of 7.7 pg/nr for the 
indoor-generated PM? 5 compared to 7.8 pg/m 3 estimated using 
the sulfur indoor-outdoor ratios (Table 3-6). 


15 








































































Table 3-8. Regressions of Indoor PM 2.5 of Outdoor Origin on Outdoor PM 2 5 Measured at the Home 


House 

N 

Slope 

SE 

Intercept 

SE 

P 

R 2 

1 

27 

0.53 

0.03 

1.47 

0.65 

0.03 

0.91 

2 

26 

0.45 

0.05 

3.06 

1.16 

0.01 

0.79 

3 

24 

0.62 

0.04 

2.34 

0.99 

0.03 

0.93 

4 

23 

0.35 

0.05 

1.71 

1.18 

0.16 

0.69 

5 

27 

0.67 

0.09 

-0.56 

1.81 

0.76 

0.69 

6 

28 

0.63 

0.03 

-0.35 

0.68 

0.61 

0.93 

7 

28 

0.81 

0.05 

-0.73 

1.25 

0.56 

0.91 

8 

6 

0.22 

0.08 

3.55 

2.14 

0.17 

0.61 

9 

24 

0.52 

0.06 

1.05 

1.31 

0.43 

0.76 

10 

22 

0.37 

0.04 

2.81 

0.86 

0.00 

0.77 

11 

8 

0.67 

0.10 

0.26 

2.46 

0.92 

0.85 

12 

28 

0.32 

0.04 

3.26 

0.71 

0.00 

0.75 

13 

7 

0.31 

0.04 

0.67 

0.64 

0.34 

0.88 

14 

27 

0.41 

0.04 

0.47 

0.68 

0.50 

0.82 

15 

27 

0.66 

0.07 

0.55 

1.15 

0.64 

0.77 

16 

28 

0.60 

0.03 

0.74 

0.52 

0.17 

0.94 

17 

26 

0.41 

0.06 

1.69 

1.18 

0.16 

0.62 

18 

6 

0.40 

0.10 

2.32 

2.15 

0.34 

0.75 

19 

22 

0.57 

0.07 

3.13 

1.40 

0.04 

0.78 

20 

22 

0.52 

0.04 

-0.30 

0.75 

0.69 

0.90 

21 

27 

0.58 

0.07 

0.47 

1.58 

0.77 

0.74 

22 

13 

0.38 

0.11 

2.95 

2.20 

0.21 

0.51 

23 

13 

0.38 

0.04 

1.88 

0.90 

0.06 

0.89 

24 

27 

0.47 

0.06 

1.03 

1.21 

0.40 

0.68 

25 

8 

0.78 

0.33 

-1.59 

9.98 

0.88 

0.39 

26 

21 

0.69 

0.05 

0.26 

1.36 

0.85 

0.90 

27 

28 

0.52 

0.04 

2.49 

0.85 

0.01 

0.86 

28 

25 

0.37 

0.06 

3.58 

1.24 

0.01 

0.59 

29 

34 

0.27 

0.05 

4.62 

1.10 

0.00 

0.43 

31 

14 

0.74 

0.05 

2.86 

1.40 

0.06 

0.95 

32 

28 

0.31 

0.05 

0.72 

0.92 

0.44 

0.56 

33 

23 

0.38 

0.06 

-0.07 

1.13 

0.95 

0.66 

34 

12 

0.59 

0.15 

0.53 

3.80 

0.89 

0.58 

36 

6 

0.26 

0.05 

-0.14 

1.23 

0.91 

0.85 

37 

20 

0.43 

0.06 

2.86 

1.16 

0.02 

0.71 

38 

17 

0.52 

0.05 

0.45 

0.67 

0.51 

0.89 

Sum/Mean 

752 

0.56 

0.01 

0.31 

0.31 

0.32 

0.67 


■ 


v 


16 







Figure 3-13. Regression of indoor PM2.5 on residential outdoor 
PM2.5 data. 

Many studies have PM 2 5 measurements indoors and outdoors but 
do not have sulfur or sulfate measurements to determine F in f. 
The RCS model provides an estimate of the average infiltration 
factor across all homes in a given study. The question arises: Is 
it possible to apply the RCS model estimate of F in fiox individual 
homes from PM measurements alone? 

The regression results show that only half (N = 18) of the homes 
had slopes that were significantly different from zero (Table 3- 
9). Also, three of the slopes were negative and two others were 
greater than 1, both unphysical results. By contrast, all 36 sulfur 
regressions had slopes that were significantly different from zero 
Comparing the slopes of the PM 2 5 regressions to the estimates of 
F ir ,f from the sulfur indoor/outdoor ratios resulted in a low R" 
value of 0.11 (Figure 3-14). 



Figure 3-14. Estimates of the infiltration factor F inf from sulfur 
indoor/outdoor ratios compared to regressions of indoor vs. outdoor fine 
particles. The regression line shown is for the 18 cases with slopes 
significantly different from zero. 


Estimates of the Contribution of Outdoor 
Air Particles to Personal Exposure 

If total personal exposure E to fine particles is the sum of 
exposures while indoors and exposures while outdoors, then the 
following equation holds: 


F -fin Cm + foul Co 


(3-2) 


where f n and f ou , are the fractions of time spent indoors and 
outdoors. 


Exposure can also be expressed as the sum of outdoor and non- 
outdoor sources: 


E - E„ +E no (3-3) 

where E a is the exposure due to outdoor sources and E no is the 
exposure due to non-outdoor sources. The exposure due to 
outdoor sources may be written as 


Eo fin Fi n f C oul "h font C, 


Olll 


(3-4) 


Now we assume that all of the time spent indoors was spent 
indoors at home where a measurement of outdoor concentration 
is available, and all of the time spent both outdoors and in transit 
is considered to involve exposure to the outdoor concentration. 
This outdoor concentration may be that measured at the home or 
at the central site if that is available. In this case, the fractions 
f out and fi„ add up to 1. We now define a factor F pex such that 

E = F pex C out + E no (3-5) 

The factor F pex plays a role with respect to personal exposure that 
F in f played with respect to indoor concentrations. Some 
investigators have labeled F pex as an “attenuation factor,” 
marking the decrease in the effect, or attenuation, of outdoor 
concentrations on personal exposure. However, this would 
imply that attenuation increases as F pex increases, whereas the 
reverse is true. We shall call F pex the “outdoor exposure factor,” 
indexing the contribution of outdoor particles to personal 
exposure. From Equations 3-3 and 3-4, we have a relationship 
for the outdoor exposure factor F pex : 

Fpex= frf'inf + font (3-6) 


Rewriting the equation as 


Fpex - Finf + foul ( 1 -Finf) (3-7) 

we see that F pex is always larger than F in f, although the difference 
is small if the fraction f out is small, as it usually is. 


17 











Table 3-9. Regressions of Indoor on Outdoor PM 2 5 by House 


House 

N 

Slope 

SE 

p-level 

Intercept 

SE 

p-level 

R 2 

1 

27 

0.37 

0.21 

0.09 

9.4 

4.1 

0.03 

0.08 

2 

26 

0.43 

0.22 

0.07 

6.0 

5.6 

0.30 

0.10 

3 

24 

0.61 

0.23 

0.01 

6.4 

6.2 

0.31 

0.21 

4 

23 

0.67 

0.76 

0.39 

26.0 

17.8 

0.16 

0.00 

5 

27 

0.72 

0.24 

0.01 

4.5 

5.0 

0.38 

0.23 

6 

28 

0.44 

0.17 

0.02 

9.0 

3.5 

0.02 

0.18 

7 

28 

0.99 

0.16 

0.00 

-3.4 

3.9 

0.39 

0.59 

8 

6 

-0.22 

0.26 

0.45 

23.9 

7.5 

0.03 

0.00 

9 

27 

0.35 

0.19 

0.08 

9.4 

4.0 

0.03 

0.08 

10 

22 

0.31 

0.05 

0.00 

4.5 

0.9 

0.00 

0.67 

11 

9 

-0.25 

0.68 

0.73 

34.4 

15.6 

0.06 

0.00 

12 

28 

0.23 

0.11 

0.04 

8.6 

2.1 

0.00 

0.12 

13 

7 

0.51 

0.25 

0.09 

1.8 

3.5 

0.64 

0.35 

14 

27 

0.33 

0.18 

0.08 

6.0 

3.3 

0.08 

0.08 

15 

28 

0.04 

0.51 

0.94 

17.5 

8.9 

0.06 

0.00 

16 

28 

0.48 

0.05 

0.00 

2.1 

0.9 

0.03 

0.76 

17 

27 

0.80 

0.24 

0.00 

-1.1 

4.4 

0.81 

0.29 

18 

21 

0.52 

0.17 

0.01 

5.9 

3.5 

0.11 

0.30 

19 

22 

0.66 

0.12 

0.00 

6.4 

2.6 

0.02 

0.58 

20 

22 

0.44 

0.04 

0.00 

2.3 

0.8 

0.01 

0.85 

21 

28 

0.47 

0.38 

0.23 

28.2 

8.9 

0.00 

0.02 

22 

13 

0.91 

0.69 

0.22 

6.6 

14.5 

0.66 

0.06 

23 

13 

-0.55 

0.85 

0.53 

36.6 

19.6 

0.09 

0.00 

24 

27 

0.42 

0.62 

0.51 

29.6 

11.9 

0.02 

0.00 

25 

8 

1.66 

1.18 

0.21 

4.6 

35.8 

0.90 

0.12 

26 

21 

0.75 

0.14 

0.00 

1.8 

3.7 

0.64 

0.58 

27 

28 

0.24 

0.60 

0.69 

30.6 

13.0 

0.03 

0.00 

28 

25 

0.36 

0.12 

0.01 

4.8 

2.4 

0.06 

0.24 

29 

34 

1.03 

0.27 

0.00 

6.8 

5.5 

0.22 

0.29 

31 

14 

0.69 

0.11 

0.00 

4.0 

3.0 

0.22 

0.76 

32 

28 

0.22 

0.18 

0.24 

9.6 

3.2 

0.01 

0.02 

33 

23 

0.34 

0.19 

0.08 

5.8 

3.6 

0.12 

0.09 

34 

12 

0.48 

0.16 

0.01 

4.7 

4.1 

0.28 

0.43 

36 

6 

0.11 

0.10 

0.31 

6.4 

2.4 

0.06 

0.07 

37 

20 

0.29 

0.12 

0.03 

6.6 

2.3 

0.01 

0.19 

38 

17 

0.44 

0.06 

0.00 

1.8 

0.9 

0.05 

0.78 


18 







Several articles have discussed this idea that personal exposure 
to particles of outdoor origin may be greater than indoor 
concentrations of such particles (Wilson and Suh, 1997; Wilson, 
Mage, and Grant 1998). The central idea is that persons spend 
some time outdoors or in vehicles where they are exposed to the 
full outdoor concentrations, thus adding to their exposures 
indoors where they are “protected” somewhat by the infiltration 
factor F in f. 

As an example, suppose f„ = 0.89, as found by the NHAPS 
study, and f out = {fraction of time outdoors ( 6 %) plus fraction of 
time in vehicles (5%)} = 0.11. Setting F inf = 0.60 as found by the 
RCS model, we find that F pex would be 0.64, an increase of 7%. 

Outdoor Exposure Factor F pex Estimated 
Using PM Measurements 

A regression of personal PM 7.5 on outdoor PM 2.5 concentrations 
provides an estimate of F pex averaged across all participants: 0.64 
+ 0.01 SE (Figure 3-15). However, the fit is poor (R : = 0.09). 
The intercept in this case (11.6+ 1.4 pg/m 3 ) is an estimate of E no , 
the average exposure due to non-outdoor sources. The 
difference between the indoor source contributions (estimated at 
8.0 pg/m 3 in Table 3-1) and the non-outdoor source contribution 
is sometimes attributed to the “personal cloud,” which in this 
case would equal 3.6 + 2.0 pg/m 1 . 



Figure 3-1 5. 24-h average fine particle personal exposures vs. outdoor 
air concentrations. 

One outlier in Figure 3-15 (personal PM 2.5 > 200 pg/m 3 ) has a 
strong influence on the slope of the line. If the outlier is 
removed, the slope decreases from 0.64 to 0.59 (Figure 3-16), 
while the intercept increases from 11.6 to 12.3. This would 
increase the personal cloud from 3.6 to 4.3 pg/m 3 . The similarity 
of the slopes for the personal vs. outdoor and indoor vs. outdoor 
regressions (0.59-0.64 compared to 0.61) suggests that the time 
spent indoors drives the relationship between personal exposure 
and outdoor concentrations. That is, the infiltration factor F in f, 
which governs the reduction of particle concentrations as they 
enter a house, is very similar to the outdoor exposure factor F pex , 


which governs the reduction in outdoor concentrations 
contributing to personal exposure. 



Figure 3-16. Personal vs. outdoor PM 2 s; one outlier removed. 

Estimating the Outdoor Exposure Factor 
Fpex Using Sulfur Measurements 

Another estimate of the outdoor exposure factor is given by the 
regression of personal sulfur exposures vs. outdoor sulfur 
concentrations (Figure 3-17). The slope is 0.49, identical to the 
slope of the indoor/outdoor sulfur regression (Figure 3-1), while 
the intercept of 90 ng/nr is less than the indoor/outdoor intercept 
of 150 ng/nr. The estimate using sulfur (R~K).78) is preferred to 
the estimate using PM 2.5 (R'=0.09). 



Outdoor Sulfur Concentration (ng/m 3 ) 

Figure 3-17. Personal vs. outdoor sulfur. 

Comparison of F pex and F inf 

Both the PM 25 and sulfur regressions fail to support the 
argument based on Equation 3-7 that F pex is larger than F m f. One 
possibility is that time spent in office buildings, which usually 
recirculate a portion of the air through filters, will involve lower 
exposures to sulfur than time spent in homes without such 
recirculation and refiltration. Some evidence for this can be 
found in the PTEAM study, where persons who worked away 


19 








from home had lower particle exposures than those who stayed 
home (Ozkaynak et al., 1996). In that case, the proper equation 
for total exposure would have to take account of the different 
concentrations in homes and offices: 


E Jinhome Cinhome foul (-out^ jinoffice (-inoffice 


where the relationship C,„ 0j! ^ ce < C in h om e would hold. This will 
reduce the magnitude of the outdoor factor. 


Comparison of Indoor-Outdoor Sulfur and 
Personal Sulfur Measurements 

Since the personal samples were collected using the 2 Lpm PEM, 
but the indoor and outdoor samples were collected using the 20 
Lpm HI, we need to address the question of how the two 
samplers agree. During the summer season, 166 co-located 
indoor and outdoor samples were collected. As shown in Figures 

■j 

3-18 and 3-19, the agreement was excellent (R" = 0.97 for both), 
with the slopes near 1 for both. This agreement between 
different monitor types is justification for comparing the 
indoor/outdoor sulfur ratio (the infiltration factor F in j) with the 
personal/outdoor sulfur ratio (the outdoor exposure factor F^). 



HI (ng/m 5 ) 


Figure 3-18. Co-located PEM 2 5 and Hl 25 sulfur concentrations 
outdoors. Summer 2000. 


K 1 



Figure 3-19. Co-located PEM 25 and Hl 25 sulfur concentrations 
indoors. Summer 2000 

The overall comparison shows that personal sulfur exposures 
were somewhat smaller than indoor concentrations at home 
(Table 3-10). When compared on a home-by-home basis, only 
33% of the participants had sulfur exposures higher than indoor 
concentrations (Table 3-11; Figure 3-20). The ratio of the mean 
personal sulfur concentration averaged over all visits to a home 
to the mean outdoor sulfur concentration ( F pex ) is 0.54 (Table 3- 
12). 



Participant 

Figure 3-20. Comparison of infiltration factor F,„f and outdoor exposure 
factor Fpex by participant. 

Finally, only 32% of 117 cases compared by home and season 
had higher personal sulfur exposures than indoor sulfur levels 
(Table 3-13). Only in the summer season was the average 
exposure higher than the average indoor concentration, but only 
by 1%. 18 of 35 homes had higher exposures than indoor 

concentrations in the summer, compared to only 8,4, and 8 cases 
in the other seasons. In most cases, F pex was less than F in f, 
contrary to the assumption that personal exposures to particles of 
outdoor origin would be larger than indoor concentrations due to 
the time spent outdoors. This supports the idea that indoor 
concentrations in buildings, where our participants spent some of 


20 


































Table 3-10. Sulfur Concentrations (ng/m 3 ) and Ratios in Matched Personal, Indoor, and Outdoor Samples 


Concentrations/ratios 

N 

Mean 

SD 

Min 

10th 

25th 

Median 

75th 

90th 

Max 

Personal 

780 

1062 

632 

137 

393 

556 

915 

1419 

1975 

3254 

Indoor 

780 

1116 

655 

123 

423 

621 

957 

1442 

2039 

3852 

Outdoor 

750 

1944 

1140 

323 

755 

1037 

1671 

2592 

3647 

5406 

Indoor/outdoor 

745 

0.59 

0.17 

0.17 

0.38 

0.48 

0.58 

0.69 

0.80 

1.08 

Personal/outdoor 

750 

0.55 

0.14 

0.16 

0.38 

0.46 

0.54 

0.63 

0.73 

1.08 

Personal/indoor 

780 

0.97 

0.23 

0.28 

0.75 

0.83 

0.92 

1.03 

1.21 

2.64 


21 








bje 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

31 

32 

33 

34 

35 

36 

37 

38 


Personal and Indoor Sulfur Concentrations (ng/m 3 ) by Subject 


N 

Personal 

SE 

Indoor 

SE 

Pers/ln 

SE 

27 

854 

71 

932 

74 

0.91 

0.01 

24 

910 

86 

1058 

86 

0.84 

0.02 

26 

1338 

108 

1508 

108 

0.88 

0.02 

25 

934 

105 

853 

72 

1.08 

0.07 

26 

1254 

141 

1196 

145 

1.07 

0.04 

27 

1082 

117 

1281 

127 

0.85 

0.02 

27 

1210 

155 

1488 

176 

0.79 

0.02 

7 

1256 

165 

1234 

145 

1.01 

0.04 

28 

1124 

141 

1130 

128 

0.97 

0.04 

27 

940 

105 

1029 

106 

0.90 

0.02 

10 

1671 

264 

1542 

251 

1.08 

0.06 

27 

892 

85 

959 

87 

0.93 

0.02 

7 

643 

112 

553 

85 

1.16 

0.10 

26 

707 

69 

720 

66 

0.97 

0.02 

27 

841 

80 

950 

87 

0.88 

0.02 

27 

951 

108 

1028 

108 

0.91 

0.01 

26 

878 

93 

976 

103 

0.91 

0.04 

7 

1398 

189 

1421 

177 

0.97 

0.04 

28 

1464 

139 

1597 

146 

0.91 

0.02 

28 

1104 

128 

1120 

120 

0.97 

0.02 

28 

1369 

149 

1211 

134 

1.12 

0.04 

14 

1329 

189 

1285 

165 

1.01 

0.04 

13 

1209 

213 

1175 

167 

0.99 

0.04 

25 

1012 

119 

880 

98 

1.15 

0.06 

8 

1686 

246 

1827 

240 

0.91 

0.03 

24 

1562 

175 

1833 

206 

0.85 

0.02 

28 

1101 

106 

1335 

133 

0.84 

0.02 

27 

896 

88 

1079 

97 

0.82 

0.02 

28 

996 

109 

989 

88 

0.99 

0.06 

14 

1469 

161 

1678 

193 

0.88 

0.02 

28 

811 

65 

607 

54 

1.43 

0.08 

25 

485 

54 

524 

65 

0.97 

0.04 

12 

1692 

224 

1746 

233 

0.97 

0.02 

6 

1056 

170 

997 

154 

1.05 

0.03 

6 

1143 

249 

658 

123 

1.77 

0.17 

20 

940 

82 

1079 

73 

0.86 

0.04 

17 

511 

41 

534 

33 

0.96 

0.04 

780 

1101 

132 

1135 

126 

0.99 

0.04 


22 






Table 3-12. Ratios of the Mean Personal Sulfur Exposure to the Mean Outdoor Sulfur Concentration (F pex ) by Subject 


Subject 

N 

Spers/Sout 

SD b 

SE b 

1 

25 

0.57 

0.34 

0.07 

2 

23 

0.49 

0.33 

0.07 

3 

21 

0.64 

0.37 

0.08 

4 

23 

0.46 

0.37 

0.08 

5 

26 

0.67 

0.54 

0.11 

6 

27 

0.51 

0.38 

0.07 

7 

27 

0.62 

0.58 

0.11 

8 

6 

0.35 

0.18 

0.08 

9 

25 

0.57 

0.54 

0.11 

10 

21 

0.46 

0.44 

0.10 

11 

8 

0.73 

0.53 

0.19 

12 

27 

0.45 

0.34 

0.06 

13 

7 

0.42 

0.26 

0.10 

14 

26 

0.44 

0.30 

0.06 

15 

27 

0.63 

0.42 

0.08 

16 

27 

0.59 

0.49 

0.09 

17 

25 

0.46 

0.38 

0.08 

18 

6 

0.50 

0.27 

0.11 

19 

22 

0.65 

0.55 

0.12 

20 

22 

0.49 

0.45 

0.10 

21 

28 

0.66 

0.57 

0.11 

22 

13 

0.54 

0.40 

0.11 

23 

14 

0.47 

0.44 

0.12 

24 

23 

0.55 

0.47 

0.10 

25 

14 

0.59 

0.36 

0.10 

26 

21 

0.60 

0.50 

0.11 

27 

28 

0.52 

0.41 

0.08 

28 

25 

0.45 

0.36 

0.07 

29 

27 

0.53 

0.47 

0.09 

31 

14 

0.77 

0.46 

0.12 

32 

28 

0.47 

0.29 

0.05 

33 

22 

0.33 

0.29 

0.06 

34 

13 

0.63 

0.37 

0.10 

35 

27 

0.60 

0.42 

0.08 

36 

6 

0.45 

0.32 

0.13 

37 

21 

0.52 

0.29 

0.06 

38 

20 

0.53 

0.33 

0.07 

Sum/Mean 

765 

0.54 a 

0.40 

0.09 


a Ratio of the mean personal sulfur concentration to the mean outdoor sulfur concentration. The mean of the ratios is also 0.54. 
b Standard Deviations (SD) and Standard Errors (SE) Calculated by Error Propagation. 


23 








Table 3-13. Comparison of Personal/Outdoor, Indoor/Outdoor, and Personal/Indoor Sulfur Ratios by House and by Season 


Summer Fall Winter Spring 


House 

N 

P/O a 

I/O 6 

P/I c 

N 

P/O 

I/O 

P/I 

N 

P/O 

I/O 

P/I 

N 

P/O 

I/O 

P/I 

1 

6 

0.50 

0.57 

0.88 

6 

0.58 

0.63 

0.93 

7 

0.60 

0.67 

0.89 

6 

0.63 

0.64 

0.99 

2 

6 

0.46 

0.57 

0.81 

6 

0.48 

0.51 

0.93 

6 

0.48 

0.63 

0.76 

5 

0.59 

0.66 

0.90 

3 

5 

0.72 

0.83 

0.86 

7 

0.64 

0.70 

0.91 

7 

0.59 

0.67 

0.88 

2 

0.59 

0.74 

0.79 

4 

2 

0.41 

0.28 

1.45 

7 

0.46 

0.47 

0.98 

7 

0.48 

0.48 

1.00 

7 

0.48 

0.39 

1.25 

5 

6 

0.57 

0.50 

1.13 

6 

0.66 

0.66 

1.01 

7 

0.66 

0.72 

0.92 

7 

0.80 

0.74 

1.09 

6 

6 

0.43 

0.57 

0.75 

7 

0.57 

0.63 

0.89 

7 

0.50 

0.59 

0.85 

7 

0.53 

0.61 

0.87 

7 

6 

0.54 

0.67 

0.81 

7 

0.70 

0.84 

0.82 

7 

0.58 

0.76 

0.77 

7 

0.71 

0.85 

0.84 

8 

6 

0.35 

0.35 

1.02 













9 

6 

0.47 

0.48 

0.97 

7 

0.58 

0.69 

0.84 

7 

0.57 

0.60 

0.94 

5 

0.70 

0.63 

1.12 

10 

5 

0.45 

0.45 

1.00 

7 

0.44 

0.57 

0.77 

7 

0.50 

0.59 

0.86 

2 

0.47 

0.50 

0.93 

11 

6 

0.74 

0.68 

1.09 





2 

0.60 

0.67 

0.90 





12 

7 

0.39 

0.40 

0.98 

7 

0.50 

0.56 

0.89 

7 

0.56 

0.62 

0.91 

6 

0.43 

0.46 

0.93 

13 

7 

0.42 

0.36 

1.16 













14 

7 

0.39 

0.39 

1.02 

6 

0.44 

0.46 

0.96 

6 

0.45 

0.47 

0.95 

7 

0.46 

0.46 

0.99 

15 

6 

0.60 

0.58 

1.04 

7 

0.63 

0.74 

0.85 

7 

0.64 

0.77 

0.82 

7 

0.66 

0.78 

0.85 

16 

6 

0.57 

0.57 

0.99 

7 

0.65 

0.72 

0.91 

7 

0.55 

0.67 

0.82 

7 

0.57 

0.62 

0.92 

17 

6 

0.45 

0.42 

1.08 

6 

0.38 

0.48 

0.80 

7 

0.47 

0.56 

0.85 

6 

0.57 

0.58 

0.98 

18 

6 

0.50 

0.51 

0.99 













19 

6 

0.59 

0.62 

0.96 

7 

0.71 

0.79 

0.90 

7 

0.71 

0.82 

0.86 

2 

0.66 

0.68 

0.97 

20 

7 

0.54 

0.53 

1.01 

6 

0.44 

0.43 

1.01 

7 

0.41 

0.47 

0.86 

2 

0.52 

0.51 

1.02 

21 

7 

0.55 

0.43 

1.27 

7 

0.74 

0.70 

1.05 

7 

0.71 

0.68 

1.05 

7 

0.75 

0.67 

1.13 

22 

6 

0.46 

0.43 

1.08 

7 

0.63 

0.64 

0.98 









23 

7 

0.47 

0.43 

1.10 

7 

0.48 

0.58 

0.83 









24 

7 

0.54 

0.41 

1.32 

6 

0.52 

0.51 

1.02 

6 

0.60 

0.57 

1.06 

4 

0.64 

0.62 

1.04 

25 

7 

0.51 

0.56 

0.92 

7 

0.71 

0.80 

0.89 









26 

6 

0.52 

0.62 

0.84 

6 

0.69 

0.78 

0.88 

7 

0.56 

0.68 

0.81 

2 

0.74 

0.78 

0.95 

27 

7 

0.45 

0.54 

0.83 

7 

0.61 

0.77 

0.80 

7 

0.58 

0.66 

0.88 

7 

0.54 

0.67 

0.81 

28 

5 

0.39 

0.40 

0.97 

7 

0.51 

0.66 

0.77 

7 

0.49 

0.61 

0.80 

6 

0.47 

0.60 

0.78 

29 

14 

0.42 

0.39 

1.07 

6 

0.62 

0.59 

1.06 

7 

0.56 

0.68 

0.82 

7 

0.62 

0.69 

0.90 

31 

7 

0.91 

0.96 

0.95 

7 

0.67 

0.82 

0.82 









32 

7 

0.49 

0.25 

1.95 

7 

0.51 

0.40 

1.29 

7 

0.56 

0.48 

1.18 

7 

0.37 

0.30 

1.22 

33 

4 

0.25 

0.20 

1.27 

6 

0.39 

0.38 

1.01 

5 

0.47 

0.50 

0.95 

7 

0.29 

0.37 

0.80 

34 

7 

0.52 

0.54 

0.97 

6 

0.75 

0.84 

0.89 









36 

6 

0.45 

0.26 

1.74 













37 





7 

0.52 

0.65 

0.80 

7 

0.54 

0.67 

0.81 

7 

0.51 

0.53 

0.96 

38 





6 

0.54 

0.54 

1.00 

7 

0.56 

0.63 

0.90 

7 

0.51 

0.50 

1.03 

Sum/Mean 

215 

0.50 

0.49 

1.01 

205 

0.58 

0.64 

0.91 

179 

0.55 

0.62 

0.89 

146 

0.56 

0.59 

0.95 


a Personal/outdoor sulfur concentration ratio (outdoor exposure factor) 
b Indoor/outdoor sulfur concentration ratio (infiltration factor) 
c Personal/indoor sulfur concentration ratio 


24 












their time, may be lower than in homes, due to recirculation and 
filtration of outdoor air. The fact that the summer season gave 
similar estimates for personal exposure and indoor 
concentrations of sulfur is additional evidence for this 
hypothesis, since air conditioning was in general use in summer 
and would provide extra filtration for the indoor sulfur particles. 

Use of the Outdoor Exposure Factor F pex to 
Calculate the Contribution to Personal 
Exposure Made by Particles of Outdoor 
Origin 

We next used our estimated outdoor exposure factors F pex for 
each subject (based on the sulfur personal/outdoor ratio) to 
calculate the contribution of particles of outdoor origin to 
personal exposure (Table 3-14; Figures 3-21 through 3-23). On 
average, particles of outdoor origin contributed 10.9 pg/m 3 
(47%) of the total personal exposure compared with 12.5 pg/m 3 
(53%) for the particles of non-outdoor origin. The range of the 
non-outdoor-generated particle contributions (5-33 pg/m 3 ) was 
much greater than the range of the outdoor-generated 
contributions (6-19 pg/m 3 ). 


The contribution of particles of non-outdoor origin to personal 
exposure can be subdivided into particles generated indoors 
while the person is at home and particles from all other sources. 



Participant 


Figure 3-21. Outdoor contributions to personal exposure. Error bars 
are standard errors calculated by propagation of error. 


50 
45 • 


40 - 



0---- • ------- - .. I. ■ , . M . ■ * , 


Participant 

Figure 3-22. Non-outdoor contributions to personal exposure. Error 
bars are standard errors calculated by propagation of error. 



Participant 

Figure 3-23. Outdoor and non-outdoor contributions to personal PM 2 5 
exposure. 

The particles from other sources include particles generated 
while the person is indoors at other locations or in a vehicle, as 
well as particles from the “personal cloud” that may be generated 
throughout the day at all locations (Yakovleva etal., 1999). The 
indoor-generated particle concentrations estimated from the F m f 
values can be multiplied by the fraction of time spent indoors at 
home (from the activity logs) to calculate the contribution of 
indoor-generated particles to personal exposure. The outdoor 
contribution is calculated by multiplying the outdoor 
concentration times the fraction of time spent outdoors. The 
contribution of indoor-generated particles at all other indoor 
locations as well as the particles from the personal cloud is then 
found by subtracting the sum of the particles of outdoor origin 
and the indoor-generated particles while at home from the total 
personal exposures (Table 3-15; Figure 3-24). 


25 




























































































Table 3-14. Estimated Contribution of Outdoor Particles to Personal Exposure (pg/m 3 ). Standard Deviations and Standard Errors Calculated by 
Propagation of Error _____ 


Subject 

N 

Outdoor contribution to 
personal exposure 

SD 

SE 

Nonoutdoor contribution to 
personal exposure 

SD 

SE 

1 

25 

9.9 

13.3 

2.7 

10.9 

16.2 

3.2 

2 

23 

11.6 

17.9 

3.7 

7.4 

20.3 

4.2 

3 

21 

15.5 

17.4 

3.8 

19.0 

33.1 

7.2 

4 

23 

9.8 

19.4 

4.0 

29.6 

36.1 

7.5 

5 

26 

12.8 

17.2 

3.4 

16.6 

29.8 

5.8 

6 

27 

9.8 

15.9 

3.1 

11.7 

25.6 

4.9 

7 

27 

14.5 

23.8 

4.6 

10.4 

29.7 

5.7 

8 

6 

9.7 

16.5 

6.7 

13.4 

17.8 

7.3 

g 

25 

11.2 

20.5 

4.1 

9.0 

22.8 

4.6 

10 

21 

7.9 

18.9 

4.1 

5.1 

19.7 

4.3 

11 

8 

16.4 

17.9 

6.3 

13.5 

21.9 

7.8 

12 

27 

8.4 

16.3 

3.1 

5.5 

16.9 

3.2 

13 

7 

5.6 

10.0 

3.8 

7.9 

13.7 

5.2 

14 

26 

7.3 

13.3 

2.6 

5.2 

14.6 

2.9 

15 

27 

10.3 

12.6 

2.4 

10.0 

16.7 

3.2 

16 

27 

9.7 

15.8 

3.0 

7.8 

17.8 

3.4 

17 

25 

8.2 

16.1 

3.2 

12.8 

23.5 

4.7 

18 

6 

10.5 

12.4 

5.1 

5.9 

13.2 

5.4 

19 

22 

13.3 

18.7 

4.0 

6.6 

20.1 

4.3 

20 

22 

9.0 

19.1 

4.1 

6.7 

20.4 

4.3 

21 

28 

14.2 

20.8 

3.9 

31.9 

24.9 

4.7 

22 

13 

10.6 

16.3 

4.5 

18.4 

23.5 

6.5 

23 

14 

9.9 

21.2 

5.7 

15.7 

26.9 

7.2 

24 

23 

9.8 

16.8 

3.5 

27.1 

26.2 

5.5 

25 

14 

17.8 

20.1 

5.4 

33.0 

52.7 

14.1 

26 

21 

14.8 

22.6 

4.9 

19.5 

27.1 

5.9 

27 

28 

10.5 

17.9 

3.4 

13.7 

22.0 

4.1 

28 

25 

8.5 

16.3 

3.3 

9.1 

18.7 

3.7 

29 

27 

9.6 

17.6 

3.4 

9.0 

19.0 

3.7 

31 

14 

18.8 

20.5 

5.5 

5.9 

24.5 

6.5 

32 

28 

7.8 

11.8 

2.2 

8.7 

14.3 

2.7 

33 

22 

5.9 

17.7 

3.8 

9.9 

21.4 

4.6 

34 

13 

15.5 

16.0 

4.4 

5.4 

17.9 

5.0 

35 

28 

9.6 

13.7 

2.6 

13.9 

10.4 

3.4 

36 

6 

10.6 

18.6 

7.6 

7.3 

19.4 

7.9 

37 

21 

9.0 

11.2 

2.5 

10.3 

16.5 

3.6 

38 

20 

7.1 

10.1 

2.3 

6.6 

11.6 

2.6 

Sum/Mean 

766 

10.9 

16.9 

4.0 

12.5 

21.8 

5.2 


26 







Table 3-15. Contributions to Personal Exposure from PM 2 5 Particles of Outdoor and Non-Outdoor Origin 


Subject 

Fraction of time at 

home 

Outdoor contribution to 

personal exposure 3 

Home contribution 15 

Other 0 

PMzs 

Personal exposure 

f d 

•out 

f ® 

•in 

“f —r 

■other 

1 

0.90 

9.9 

5.0 

5.9 

20.8 

0.48 

0.24 

0.28 

2 

0.77 

11.6 

2.1 

5.3 

19.0 

0.61 

0.11 

0.28 

3 

0.89 

15.5 

3.7 

15.2 

34.5 

0.45 

0.11 

0.44 

4 

0.83 

10.0 

26.4 

3.2 

39.6 

0.25 

0.67 

0.08 

5 

0.78 

12.8 

4.7 

12.0 

29.5 

0.43 

0.16 

0.41 

6 

0.88 

9.8 

5.2 

6.5 

21.5 

0.46 

0.24 

0.30 

7 

0.84 

14.5 

1.3 

9.1 

24.9 

0.58 

0.05 

0.37 

8 

0.75 

9.7 

6.1 

7.3 

23.1 

0.42 

0.26 

0.32 

9 

0.73 

11.1 

3.5 

5.5 

20.2 

0.55 

0.17 

0.27 

10 

0.90 

7.9 

0.6 

4.5 

13.0 

0.61 

0.05 

0.35 

11 

0.85 

16.4 

10.0 

3.6 

29.9 

0.55 

0.33 

0.12 

12 

0.84 

8.4 

3.3 

2.3 

14.0 

0.60 

0.24 

0.16 

13 

0.89 

5.6 

3.4 

4.5 

13.5 

0.41 

0.25 

0.33 

14 

0.86 

7.3 

3.6 

1.7 

12.6 

0.58 

0.29 

0.13 

15 

0.92 

9.8 

6.8 

3.2 

19.8 

0.49 

0.34 

0.16 

16 

0.79 

9.7 

-0.4 

8.2 

17.5 

0.55 

-0.02 

0.47 

17 

0.88 

8.2 

3.7 

9.1 

21.0 

0.39 

0.18 

0.43 

18 

0.84 

10.5 

1.2 

4.7 

16.4 

0.64 

0.07 

0.29 

19 

0.87 

13.3 

4.3 

2.4 

19.9 

0.67 

0.22 

0.12 

20 

0.71 

9.0 

0.8 

5.9 

15.6 

0.58 

0.05 

0.38 

21 

0.61 

14.6 

15.5 

16.5 

46.5 

0.31 

0.33 

0.35 

22 

0.88 

10.6 

12.4 

6.0 

29.0 

0.37 

0.43 

0.21 

23 

0.70 

10.0 

10.7 

5.0 

25.7 

0.39 

0.42 

0.19 

24 

0.81 

9.8 

21.5 

5.6 

36.9 

0.27 

0.58 

0.15 

25 

0.69 

19.3 

22.6 

10.5 

52.4 

0.37 

0.43 

0.20 

26 

0.83 

14.6 

2.2 

17.3 

34.1 

0.43 

0.06 

0.51 

27 

0.87 

10.5 

19.7 

-6.0 

24.2 

0.43 

0.81 

-0.25 

28 

0.84 

8.5 

0.9 

8.3 

17.6 

0.48 

0.05 

0.47 

29 

0.91 

9.6 

16.1 

-7.1 

18.6 

0.52 

0.87 

-0.38 

31 

0.87 

18.8 

-0.2 

6.0 

24.6 

0.76 

-0.01 

0.24 

32 

0.67 

7.8 

4.9 

3.8 

16.5 

0.47 

0.30 

0.23 

33 

0.95 

5.9 

5.0 

4.9 

15.8 

0.37 

0.32 

0.31 

34 

0.83 

14.9 

1.0 

4.4 

20.3 

0.73 

0.05 

0.22 

35 

0.97 

9.5 

11.6 

2.3 

23.4 

0.41 

0.50 

0.10 

36 

0.74 

10.6 

2.2 

5.1 

17.9 

0.59 

0.12 

0.28 

37 

0.87 

9.1 

1.1 

9.2 

19.4 

0.47 

0.06 

0.47 

38 

0.68 

7.1 

0.2 

6.4 

13.6 

0.52 

0.01 

0.47 

Mean 

0.82 

10.9 

6.6 

5.9 

23.3 

0.49 

0.25 

0.26 

SD 

0.08 

3.3 

7.0 

4.8 

9.3 

0.12 

0.22 

0.18 


a product of F pex and the mean residential outdoor concentration 
b product of the indoor-generated concentration and the fraction of time at home 

c sum of particle concentrations indoors at other locations times the fraction of time spent there plus contributions from the personal cloud; obtained 
by subtraction of outdoor and home contributions from total personal exposure 
d Proportion of personal exposure contributed by outdoor-generated particles 
e Proportion of personal exposure contributed by indoor-generated particles while at home 

f Proportion of personal exposure contributed by indoor-generated particles while indoors other than at home plus personal activities throughout the 
day 


27 








Participant 


Figure 3-24. Outdoor and non-outdoor contributions to personal PM 25 
exposure. The non-outdoor contribution is divided into indoor-generated 
PM 2 5 encountered while at home and the sum of indoor-generated PM 2 5 
while away from home and PM 2 5 due to the personal cloud 

From Table 3-15, the contribution of at-home indoor-generated 
particles to personal exposure varied widely, from about 0 to 26 

i ■} 

pg/m (mean 6.6 pg/m ). The contribution of particles generated 
while the person was away from home (either from exposures 
indoors at other locations or from personal activities throughout 
the day) ranged from -7 to 17 pg/m' (mean 5.9 gg/nr). Two of 
the 37 participants had negative values for this variable due to 
uncertainties in estimating the outdoor and home contributions. 
On average, the home contribution plus the contribution of the 
personal cloud and exposures in locations other than the home 
added up to a bit more than the contribution from outdoor¬ 
generated particles (12.5 gg/nr vs. 10.9 gg/nr)- On an individual 
basis, however, some participants have virtually all of their 
exposures contributed from outdoor particles, while others have 
most of their exposure from indoor-generated or personal cloud 
particles (Figure 3-24). 

Relationship Between Outdoor 
Concentrations and the Contribution to 
Personal Exposure of Particles of Outdoor 
Origin 

As described in the Introduction, our ultimate aim is to detennine 
the relationship between personal exposure to particles of 
outdoor origin and ambient measurements. We regressed our 
estimate of the outdoor contribution to personal exposure on the 
concentrations just outside each home (Table 3-16). Adjusted R 2 
values ranged from 0.42-0.93 (median of 0.81). The overall R 2 
was 0.71 (N = 750). When the regression was run against the 
HI at the central site, the overall R 2 was reduced to 0.64 (N = 
735). The regressions of exposure to particles of outdoor origin 
against central site FRM concentrations is the one that interests 
epidemiologists, who use values at the central site to estimate 
personal exposure. For this set of regressions, the mean R' was 
0.60 (N = 743), with a range from 0.19 to 0.88 (median = 0.73). 


• • 2 i 

Again, we caution that our assumptions force high R" values, and 
these should be considered upper bounds for the true R' values. 

Tables 3-16 and 3-17 provide clear evidence of some 
degradation in the relationship between personal exposure to 
particles of outdoor origin and the outdoor concentrations as we 
consider measurements made further from the home. 30 of the 
37 participants showed higher correlations with outdoor 
concentrations measured just outside the home than with 
concentrations measured at the central site by the FRM. The 
range in R 2 differences was -0.24 to +0.52, with an average 
difference of 0.11. 

Would these regressions show improved results if we considered 
each season separately? There would be an advantage perhaps in 
that the infiltration factor would probably be less variable within 
a season than across seasons. However, a drawback would be 
the limitation to no more than 7 observations per household per 
season. After testing this on the seasonal values, we found that 
the drawback of fewer observations outweighed the advantage of 
reducing cross-season variability. That is, adjusted R 2 values by 
season included many that were well below the minimum of 0.19 
observed for the full set of year-round observations. The 
adjusted R 2 results for seasonal regressions with the residential 
outdoor and central-site outdoor monitors are shown in Figures 
A-l to A-3 in the Appendix. 

Use of Reported Time in Indoor and 
Outdoor Microenvironments to Predict the 
Outdoor Exposure Factor F pex from the 
Infiltration Factor F in f 

In some studies, personal exposure is not measured; therefore, a 
general theory has been developed using time-activity data and 
measurements of indoor and outdoor concentrations as a way of 
estimating personal exposure. Equation 3-6 provides a way to 
predict the outdoor exposure factor F ^ from the fractions of 
time indoors and outdoors and the measured infiltration factor 
F in f. Since we also have a measured value of F^, using the 
sulfur exposure/outdoor ratio, we can compare values predicted 
by Equation 3-6 to measured values. 

From the daily time-activity questionnaires, we can find the time 
spent in various activities (Table 3-18). We define the fraction 
of time outdoors f ou , as the time either outdoors or in travel, with 
a mean value of 10%. The fraction of time indoors f n (mean 
90%) includes indoors at home and all other locations. For each 
participant and each day we can calculate F pex from Equation 3- 
6 . Table 3-18 and Figure 3-25 show that the predicted value of 
F pex exceeds the measured value at nearly all points of the 
distribution. Although the R 2 is 0.57, we can almost match it 


28 













































Table 3-16. Regression of Personal Exposure to Particles of Outdoor Origin on Outdoor Concentrations Measured Near Residence by Harvard 
Impactor (HI) 


Person 

N 

Slope 

SE 

Inter 

SE 

P 

R 2 

1 

25 

0.48 

0.04 

1.47 

0.83 

0.09 

0.84 

2 

24 

0.44 

0.05 

1.36 

1.20 

0.27 

0.77 

3 

21 

0.50 

0.05 

3.12 

1.29 

0.03 

0.84 

4 

23 

0.44 

0.05 

0.46 

1.21 

0.71 

0.76 

5 

27 

0.68 

0.06 

-0.08 

1.28 

0.95 

0.82 

6 

27 

0.56 

0.05 

-0.93 

0.98 

0.35 

0.84 

7 

27 

0.65 

0.07 

-0.81 

1.66 

0.63 

0.79 

8 

6 

0.31 

0.04 

1.10 

1.24 

0.42 

0.91 

9 

25 

0.53 

0.08 

0.76 

1.76 

0.67 

0.63 

10 

21 

0.35 

0.03 

1.83 

0.66 

0.01 

0.84 

11 

9 

0.32 

0.03 

2.45 

0.69 

0.00 

0.80 

12 

27 

0.42 

0.10 

0.00 

1.39 

1.00 

0.77 

13 

7 

0.72 

0.12 

-0.11 

3.11 

0.97 

0.74 

14 

27 

0.44 

0.04 

-0.12 

0.70 

0.87 

0.83 

15 

27 

0.67 

0.05 

-0.90 

0.97 

0.36 

0.85 

16 

27 

0.55 

0.03 

0.59 

0.55 

0.30 

0.93 

17 

25 

0.40 

0.07 

1.29 

1.25 

0.31 

0.59 

18 

6 

0.45 

0.09 

1.02 

2.03 

0.64 

0.81 

19 

22 

0.54 

0.04 

2.43 

0.95 

0.02 

0.88 

20 

22 

0.55 

0.04 

-1.10 

0.73 

0.15 

0.92 

21 

28 

0.55 

0.04 

-1.10 

0.73 

0.15 

0.85 

22 

13 

0.48 

0.10 

1.14 

2.19 

0.61 

0.63 

23 

14 

0.48 

0.06 

0.02 

1.36 

0.99 

0.83 

24 

23 

0.52 

0.04 

0.68 

0.83 

0.42 

0.87 

25 

14 

0.69 

0.11 

-2.20 

3.21 

0.51 

0.76 

26 

21 

0.60 

0.06 

0.20 

1.57 

0.90 

0.83 

27 

28 

0.40 

0.03 

2.48 

0.61 

0.00 

0.88 

28 

25 

0.36 

0.04 

1.90 

0.75 

0.02 

0.78 

29 

27 

0.40 

0.06 

2.38 

1.19 

0.06 

0.62 

31 

14 

0.58 

0.06 

4.00 

1.69 

0.04 

0.88 

32 

28 

0.30 

0.07 

2.77 

1.17 

0.03 

0.42 

33 

22 

0.27 

0.04 

1.39 

0.78 

0.09 

0.67 

34 

13 

0.55 

0.17 

1.77 

4.34 

0.69 

0.44 

35 

27 

0.40 

0.06 

2.56 

1.22 

0.05 

0.60 

36 

6 

0.46 

0.12 

-0.46 

3.01 

0.89 

0.74 

37 

21 

0.43 

0.07 

1.61 

1.28 

0.23 

0.64 

38 

21 

0.50 

0.05 

0.46 

0.76 

0.55 

0.81 

Sum/Mean 

750 

0.53 

0.01 

0.26 

0.26 

0.40 

0.71 


29 






Table 3-17. Regression of Personal Exposure to Particles of Outdoor Origin on Outdoor Concentrations Measured at Central Site by Federal 
Reference Method (FRM) 


Subject 

N 

Slope 

SE 

p (slope) 

Intercept 

SE 

p (Int) 

R 2 

1 

25 

0.58 

0.06 

0.00 

0.95 

1.08 

0.39 

0.77 

2 

24 

0.45 

0.06 

0.00 

2.71 

1.16 

0.03 

0.74 

3 

21 

0.48 

0.06 

0.00 

5.81 

1.35 

0.00 

0.76 

4 

23 

0.40 

0.05 

0.00 

1.49 

1.19 

0.22 

0.73 

5 

27 

0.76 

0.10 

0.00 

0.24 

1.75 

0.89 

0.69 

6 

27 

0.50 

0.04 

0.00 

0.39 

0.87 

0.65 

0.84 

7 

27 

0.57 

0.07 

0.00 

2.07 

1.77 

0.25 

0.69 

8 

6 

0.36 

0.06 

0.00 

0.49 

1.54 

0.77 

0.88 

9 

25 

0.55 

0.09 

0.00 

1.11 

1.82 

0.55 

0.59 

10 

21 

0.46 

0.06 

0.00 

1.05 

0.97 

0.29 

0.74 

11 

9 

0.62 

0.18 

0.01 

3.08 

4.32 

0.50 

0.57 

12 

27 

0.35 

0.04 

0.00 

2.58 

0.67 

0.00 

0.77 

13 

7 

0.54 

0.15 

0.01 

-0.92 

1.88 

0.64 

0.67 

14 

27 

0.48 

0.05 

0.00 

-0.11 

0.77 

0.88 

0.8 

15 

27 

0.71 

0.05 

0.00 

0.25 

0.83 

0.76 

0.87 

16 

27 

0.58 

0.07 

0.00 

0.85 

1.11 

0.45 

0.74 

17 

25 

0.35 

0.10 

0.00 

2.09 

1.77 

0.25 

0.34 

18 

6 

0.52 

0.22 

0.08 

0.08 

4.54 

0.99 

0.47 

19 

22 

0.47 

0.06 

0.00 

5.18 

1.22 

0.00 

0.71 

20 

22 

0.58 

0.07 

0.00 

-1.47 

1.32 

0.28 

0.77 

21 

28 

0.51 

0.14 

0.00 

4.08 

2.93 

0.17 

0.33 

22 

13 

0.43 

0.12 

0.00 

1.64 

2.64 

0.55 

0.5 

23 

14 

0.45 

0.06 

0.00 

1.39 

1.20 

0.27 

0.83 

24 

23 

0.41 

0.09 

0.00 

2.48 

1.68 

0.15 

0.49 

25 

14 

0.68 

0.10 

0.00 

-1.56 

3.12 

0.63 

0.76 

26 

21 

0.60 

0.07 

0.00 

1.56 

1.70 

0.37 

0.78 

27 

28 

0.41 

0.04 

0.00 

3.01 

0.73 

0.00 

0.82 

28 

25 

0.34 

0.04 

0.00 

2.23 

0.83 

0.01 

0.73 

29 

27 

0.37 

0.06 

0.00 

3.61 

1.14 

0.00 

0.56 

31 

14 

0.60 

0.09 

0.00 

5.77 

2.30 

0.03 

0.76 

32 

28 

0.31 

0.06 

0.00 

2.66 

1.09 

0.02 

0.47 

33 

22 

0.33 

0.06 

0.00 

0.85 

1.00 

0.41 

0.59 

34 

13 

0.57 

0.11 

0.00 

0.93 

2.95 

0.76 

0.68 

35 

27 

0.23 

0.08 

0.01 

6.06 

1.40 

0.00 

0.19 

36 

6 

0.56 

0.09 

0.00 

-2.39 

2.20 

0.34 

0.88 

37 

21 

0.46 

0.06 

0.00 

1.99 

1.02 

0.07 

0.73 

38 

21 

0.53 

0.08 

0.00 

0.38 

1.10 

0.73 

0.67 

Sum/ 

Mean 

750 

0.52 

0.02 

0.00 

1.29 

0.31 

0.00 

0.60 


v 


30 







Table 3-18. Time (in Minutes) Spent in Various Activities/Locations 


Activity/Location 

N 

Mean 

SD 

Min 

10th 

25th 

Median 

75th 

90th 

Max 

Indoors at home 

727 

1179 

181 

495 

915 

1065 

1215 

1320 

1395 

1440 

Cooking 

727 

98 

72 

0 

15 

45 

75 

135 

195 

480 

Cleaning 

727 

39 

45 

0 

0 

0 

30 

60 

105 

210 

Grooming 

727 

57 

47 

0 

15 

30 

45 

75 

120 

480 

Other indoor locations 

727 

90 

82 

0 

0 

30 

75 

120 

200 

540 

Travel 

727 

78 

61 

0 

0 

30 

60 

105 

165 

345 

Outdoors 

727 

67 

63 

0 

0 

15 

45 

105 

150 

390 

Unknown 

727 

11 

22 

0 

0 

0 

0 

15 

30 

135 

Exposed to smoke 

727 

14 

44 

0 

0 

0 

0 

0 

45 

375 

Outdoors + travel 

727 

145 

103 

0 

30 

60 

120 

210 

285 

600 

Indoors 

727 

1295 

103 

840 

1155 

1230 

1320 

1380 

1410 

1440 

foui (fraction of time outdoors or in travel) 

727 

0.10 

0.07 

0.00 

0.02 

0.04 

0.08 

0.15 

0.20 

0.42 

f in (fraction of time indoors) 

727 

0.90 

0.07 

0.58 

0.80 

0.85 

0.92 

0.96 

0.98 

1.00 

Infiltration factor F m , (indoor/outdoor S) 

727 

0.59 

0.16 

0.17 

0.38 

0.47 

0.58 

0.68 

0.79 

1.06 

Measured outdoor factor F pex (personal/outdoor S) 

727 

0.55 

0.14 

0.16 

0.38 

0.45 

0.54 

0.63 

0.73 

1.08 

Predicted outdoor factor F pex from Equation 3-6 

727 

0.63 

0.15 

0.19 

0.44 

0.53 

0.63 

0.72 

0.82 

1.05 


31 







by using only F in f (not F pex ) to estimate personal exposure 
(Figure 3-26). The latter regression has a higher slope (0.85 vs. 
0.80), a lower intercept (0.12 vs. 0.19) and an R 2 of 0.49, 
indicating that the extra information from the time-activity diary 
was not sufficient or was not precise enough to produce a better 
result than simply using the indoor/outdoor sulfur ratio alone. 



Figure 3-25. Predicted value of the outdoor exposure factor F pex using 
Equation 3-6 compared to the measured value using the personal/outdoor 
sulfur ratio. 



Figure 3-26. Predicted personal exposure to PM 2 5 using only F mf . 

Equation 3-2 relates personal exposure to the time-weighted 
averages of the indoor and outdoor microenvironments. We 
have measurements of fine particle concentrations both indoors 
and outdoors, but they are only 24-h averages and are not 
necessarily the actual concentrations experienced by the persons 
at the time they were in those microenvironments. Also, fine 
particle concentrations were not measured in some locations such 
as the car and workplace. If we assign the measured residential 
outdoor value to all the outdoor and transport 
microenvironments, and the measured indoor concentrations in 
the home to all the other indoor locations, we can test how well 
Equation 3-2 does in predicting personal exposure by comparing 
with the measured personal exposure. Results (Figure 3-26) 


show a moderate ability of Equation 3-2 to predict exposure, 
with an adjusted R 2 of 0.49 (N=753). However, the average 
personal exposure predicted from Equation 3-2 was only 19.4 
pg/nr compared to the observed average exposure of 23.1 pg/m 3 . 
The difference of 3.7 pg/nT provides an independent estimate of 
the magnitude of the personal cloud, and this value agrees well 
with the earlier estimates above. The inability of Equation 3-2 to 
estimate personal exposure has been shown previously 
(Ozkaynak et al., 1996; Pellizzari et al., 1999). 

Estimating P and k 

Calculating Average Values of P and k 

Equation 1-3 for the infiltration factor is nonlinear in the air 
exchange rate a. Therefore if we plot our measured 24-h average 
indoor/outdoor sulfur ratios versus the measured air exchange 
rate, we can solve for the overall average P and k by minimizing 
some appropriate function. We tried minimizing the squares of 
the differences between the measured and modeled 
concentrations (ordinary Gaussian least squares approach) and 
also tried minimizing the absolute differences (a procedure 
giving less weight to outliers). Both approaches gave almost 
identical results, so we report only the results from the ordinary 
least squares approach. Figure 3-27 gives the results for all the 
individual 24-h measurements (N = 720). The estimate for the 
penetration coefficient P averaged across all measurements is 
0.85 (0.02 SE). The estimate for the average deposition rate k is 
0.22 h' 1 (0.01 SE). The R" value was moderately high at 0.45. 



Figure 3-27. Nonlinear least-squares fit to the indoor/outdoor sulfur ratio 
vs. the air exchange rate. Bounding curves are + 1 SE. 


A second (linear) approach to calculating average values of P 
and k follows from the simple time-averaged mass balance 
equation for sulfur with no indoor sources: 

SJS oul = Pa/(a+k) (3-9) 

This equation is not linear for one of the unknowns ( k ), and it 
also mixes the values of P and k together in one factor. We can 
partially separate P from k by inverting the equation (Long et al., 
2001 ): 


32 
















S ou /S in = k/(Pa) +1/P 


(3-10) 


6 


Note that writing the equation this way isolates P in a single 
term, the intercept of the regression. This value can then be used 
to calculate k from the other term. Since the inverse of the air 
exchange rate a is the residence time r, we can put this equation 
into the form of a linear regression on r: 


S ou /S in = (k/P)z + 1/P (3-11) 

The intercept of the regression will be our best estimate of P and 
the slope will lead to an estimate of k (based on our value for P). 
One problem with this approach is that the intercept is always an 
extension of a line determined by points that may be far from the 
intercept. For example, participants 4 and 6 in the following 
graphs have no values of r lower than 2 h or 1 h, respectively, so 
that the extrapolation of the line to a value of 0 goes well beyond 
the domain where all the points lie. This will lead to more 
variation and a greater uncertainty in our estimates of P. 

As an initial check on whether the sulfate data provides useful 
data for determining P and k we can run the regression on the 
full data set (Figure 3-28). 



Figure 3-28. Regression of the outdoor/indoor sulfur ratio vs. residence 
time. 


However, there are four very influential outliers at unusually 
high residence times (air exchange rates < 0.1 h '). These are 
unlikely values given other measurements in the same house. If 
we rerun the regression without these outliers we obtain 
somewhat larger values for P and k, as well as an improved R~ 
(Figure 3-29). The overall average deposition rate k = 0.23 (0.01 
SE) h' 1 while the overall average penetration coefficient P = 



Residence time t (h) 


Figure 3-29. Same regression as in Figure 3-28 without four outliers. 


0.81 (0.03 SE). Since measurement error generally leads to 
lower slopes and higher intercepts, it may be that these estimates 
are lower bounds for the actual average k and P. These linear 
estimates agree well with the nonlinear estimates of 0.22 h' 1 for k 
and 0.85 for P. 


Calculating Individual Home Values of P 
and k 

In general the different ways of analyzing the data do not 
disagree violently, and therefore we can go ahead and try to 
calculate k and P for the individual homes. We first assume that 
P and k do not change greatly across seasons for any home. This 
is unlikely to be the case if certain household characteristics such 
as window opening, use of fans and filters, changes by season. 
Nonetheless, by making this assumption, we can make use of all 
measurements for each house in a single regression. We can 
employ a nonlinear fit to the observed values of the 
indoor/outdoor sulfur ratio (see Equations 1-3 and 1-4), 
calculating the best value of P and k for each home averaged 
across all seasons (Table 3-19). The results are mixed, with R 2 
values ranging from zero to 0.94. 32 of 37 estimates for P were 
significantly different from zero, but only 16 estimates of k were 
significantly different from zero (significant results shown in 
boldface). Of the 32 significant estimates for P, three were 
considerably greater than 1, a physical impossibility. The range 
of significant P estimates was from 0.52 to 1.40, but the 
interquartile range was more tightly clustered between 0.66 and 
0.98. 


There are 5 values for P (3 of them significant) that are well 
above 1, ranging from 1.16 to 2.02. We can rerun the 
regressions for these 5 cases bounding P from above at 1 (Table 
3-20). All five of the new estimates of k are significantly 
different from zero, compared to three when P was unbounded. 
The R 2 values are decreased slightly compared to the unbounded 
case, but remain quite high, from 0.31 to 0.81. 


33 







Table 3-19. Estimates of P and k for Individual Homes Using Nonlinear Fit to the Indoor/Outdoor Sulfur Ratio 


House 

N 

P 

SE 

P (P) 

k 

SE 

P W 

R 2 

1 

22 

0.85 

0.10 

0.000 

0.26 

0.13 

0.049 

0.29 

2 

23 

0.61 

0.04 

0.000 

-0.01 

0.04 

0.861 

0.00 

3 

28 

0.83 

0.04 

0.000 

0.12 

0.04 

0.012 

0.27 

4 

21 

0.61 

0.15 

0.001 

0.09 

0.09 

0.326 

0.17 

5 

20 

1.16 

0.15 

0.000 

0.41 

0.12 

0.003 

0.53 

6 

26 

0.67 

0.07 

0.000 

0.06 

0.06 

0.377 

0.04 

7 

25 

0.88 

0.04 

0.000 

0.14 

0.05 

0.013 

0.32 

8 

6 

0.44 

0.23 

0.134 

0.06 

0.16 

0.746 

0.05 

9 

21 

0.80 

0.09 

0.000 

0.17 

0.08 

0.036 

0.35 

10 

25 

0.71 

0.08 

0.000 

0.14 

0.07 

0.065 

0.23 

11 

8 

0.67 

0.05 

0.000 

-0.01 

0.03 

0.725 

0.02 

12 

28 

0.91 

0.12 

0.000 

0.29 

0.10 

0.006 

0.51 

13 

7 

0.30 

0.08 

0.013 

-0.04 

0.04 

0.358 

0.11 

14 

27 

0.45 

0.05 

0.000 

0.00 

0.04 

0.994 

0.00 

15 

24 

1.03 

0.18 

0.000 

0.23 

0.13 

0.097 

0.25 

16 

28 

0.67 

0.03 

0.000 

0.02 

0.03 

0.557 

0.02 

17 

19 

0.60 

0.07 

0.000 

0.03 

0.02 

0.192 

0.15 

18 

6 

1.17 

0.86 

0.246 

0.37 

0.50 

0.498 

0.40 

19 

27 

1.00 

0.07 

0.000 

0.35 

0.10 

0.002 

0.51 

20 

27 

0.52 

0.04 

0.000 

0.02 

0.03 

0.505 

0.02 

21 

26 

0.88 

0.08 

0.000 

0.21 

0.07 

0.004 

0.53 

22 

13 

0.97 

0.23 

0.001 

0.28 

0.16 

0.106 

0.48 

23 

13 

0.82 

0.10 

0.000 

0.50 

0.15 

0.007 

0.74 

24 

27 

0.77 

0.10 

0.000 

0.18 

0.08 

0.033 

0.28 

25 

8 

1.37 

0.15 

0.000 

0.48 

0.12 

0.007 

0.94 

26 

22 

0.83 

0.07 

0.000 

0.13 

0.07 

0.071 

0.22 

27 

27 

0.77 

0.04 

0.000 

0.15 

0.06 

0.021 

0.27 

28 

24 

0.75 

0.08 

0.000 

0.13 

0.06 

0.046 

0.29 

29 

31 

1.46 

0.38 

0.001 

0.69 

0.31 

0.033 

0.53 

32 

28 

0.60 

0.20 

0.006 

0.18 

0.15 

0.246 

0.15 

33 

20 

0.55 

0.09 

0.000 

0.22 

0.12 

0.086 

0.37 

34 

10 

2.02 

2.05 

0.352 

0.93 

1.36 

0.511 

0.39 

37 

19 

0.79 

0.06 

0.000 

0.12 

0.04 

0.004 

0.55 

38 

15 

0.68 

0.06 

0.000 

0.07 

0.03 

0.048 

0.37 

31 

14 



Did not converge 





36 

5 



Did not converge 





35 

0 



Same as house 29 






34 
















Table 3-20. Values for k when P is Bound from Above by 1 


Subject 

N 

R 2 

k 

SE 

25 

8 

0.81 

0.21 

0.03 

29 

31 

0.48 

0.33 

0.03 

34 

10 

0.31 

0.26 

0.03 

18 

6 

0.40 

0.28 

0.02 

5 

20 

0.50 

0.28 

0.03 


35 







A similar approach to the above nonlinear use of the 
indoor/'outdoor sulfur ratio is to use the linear equation involving 
the inverse of that ratio, as well as the inverse of the air exchange 
rate (Table 3-21). 24 homes had both slope and intercept 

significantly different from zero (shown in boldface). 

Five values of P were well above 1, so the regressions were 
rerun bounding P from above by 1 (Table 3-22). All 5 new 
estimates of A are significantly different from zero, compared to 
none when P was unbounded. The R 2 values are decreased 
slightly compared to the unbounded case, but remain quite high, 
from 0.32 to 0.82. 

The estimates of P and k from the nonlinear approach (Table 3- 
19) are compared to the estimates from the linear approach 
(Table 3-21) in Figures 3-30 and 3-31. Only values significantly 
different from zero are included. Figure 3-30 suggests that the 
nonlinear approach gave consistently smaller estimates of P at 
the high end of the range. In fact, of the five estimates exceeding 
unity due to the linear approach, all five were lower, and two 
were less than one, in the nonlinear approach. 

The median value for P was 0.81 (interquartile range 0.66 to 
0.90). The median for A'was 0.24 h' 1 (interquartile range 0.12 to 
0.35 h 1 ). 

Having calculated P and A for each home from the linear 
regression of the outdoor/indoor sulfur ratio vs. the residence 
time, the estimated average infiltration factor for each home can 
be calculated from the equation 

F in f - Pa/(a+k) (3-12) 


where a in this case is the arithmetic, geometric, or harmonic 
average of the air exchange rates for each home. 



Figure 3-30. Comparison of estimates of P from the linear and nonlinear 
approaches described in the text. Only values significantly different from 
zero are plotted (N = 32 homes). 


1.2 

1 


0.8 

£. 0.6 

jc 

0.4 

0.2 

0 

Figure 3-31. Comparison of the estimates of A from the linear and 
nonlinear approaches described in the text. Only values significantly 
different from zero are plotted (N = 24 homes). 

Estimating F inf from Individual Values of P 
and k 

These estimated values of P and A from the two approaches 
(linear and nonlinear) can be used to estimate F,„/for each home. 
These estimates are compared to the observed indoor/outdoor 
sulfur ratio in Figure 3-32. Although the excellent agreement 
(R 2 = 0.96 for the nonlinear estimate and 0.99 for the linear 
estimate) of the F mf estimates may be an artifact due to the 
related nature of the three calculations, at least this agreement 
suggests that the P and A values used to estimate F inf from both 
the linear and nonlinear regressions (which both involve the air 
exchange rate or its inverse) are at least consistent with the 
measured indoor/outdoor sulfur ratios (which do not involve the 
air exchange rate). However, the many cases in which neither 
the linear nor the nonlinear approach gave values for A 
significantly different from zero, and the several cases in both 
approaches in which values of P were greater than unity, 
suggests that the assumption of constant values for A and P 
across seasons for each home is violated. 

i 

^ 0.9 

a 

2 0.8 

1 „ 0.7 

1 § 0.6 

" 8 0.5 

j. a, 

2 ? 0.4 

2 0.2 

5 - 

0 

0 0.2 0.4 0.6 0.8 1 

Fm, from Sulfur Indoor-Outdoor Ratio 

Figure 3-32. Comparison of the infiltration factor (F inf ) estimates from the 
simple ratio of indoor sulfur to outdoor sulfur by home vs. the nonlinear 
regression of the same ratio using the measured air exchange rates and 
the linear regression of the inverse ratio (outdoor/indoor) against the 
residence time. 



■ k(lin) 
• k (nlin) 


9 - 


■ ■ • . 


29 25 23 22 32 21 19 15 4 12 1 28 24 6 27 28 7 J7 10 » 3 33 38 17 

House ID 


36 
























Table 3-21. Results of Linear Regressions of the Outdoor/Indoor Sulfur Ratio on Residence Time for 36 Homes 


House 

N 

1/P 

SE 

P 

SE 

k/P 

SE 

k 

R 2 

1 

22 

1.19 

0.13 

0.84 

0.09 

0.31 

0.09 

0.26 

0.34 

2 

23 

1.66 

0.11 

0.60 

0.04 

0.00 

0.07 

0.00 

0.00 

3 

28 

1.24 

0.06 

0.81 

0.04 

0.11 

0.04 

0.09 

0.20 

4 

21 

1.08 

0.45 

0.93 

0.39 

0.32 

0.11 

0.29 

0.28 

5 

20 

1.12 

0.21 

0.90 

0.17 

0.25 

0.09 

0.22 

0.25 

6 

26 

1.47 

0.15 

0.68 

0.07 

0.10 

0.08 

0.07 

0.02 

7 

25 

1.14 

0.05 

0.88 

0.04 

0.16 

0.05 

0.14 

0.33 

8 

6 

2.25 

1.36 

0.44 

0.27 

0.14 

0.33 

0.06 

0.00 

9 

21 

1.45 

0.13 

0.69 

0.06 

0.14 

0.04 

0.10 

0.32 

10 

25 

1.51 

0.15 

0.66 

0.06 

0.17 

0.06 

0.11 

0.20 

11 

8 

1.49 

0.10 

0.67 

0.05 

-0.01 

0.04 

-0.01 

0.00 

12 

28 

1.13 

0.16 

0.89 

0.13 

0.32 

0.06 

0.28 

0.52 

13 

7 

3.03 

0.79 

0.33 

0.09 

-0.05 

0.17 

-0.02 

0.00 

14 

27 

2.20 

0.24 

0.45 

0.05 

0.04 

0.07 

0.02 

0.00 

15 

24 

0.85 

0.18 

1.18 

0.26 

0.30 

0.09 

0.35 

0.30 

16 

28 

1.51 

0.07 

0.66 

0.03 

0.03 

0.05 

0.02 

0.00 

17 

19 

1.66 

0.20 

0.60 

0.07 

0.07 

0.03 

0.04 

0.22 

18 

6 

0.99 

0.65 

1.01 

0.67 

0.29 

0.19 

0.29 

0.19 

19 

27 

1.00 

0.06 

1.00 

0.06 

0.36 

0.06 

0.35 

0.60 

20 

27 

1.93 

0.14 

0.52 

0.04 

0.04 

0.04 

0.02 

0.00 

21 

26 

0.96 

0.12 

1.05 

0.13 

0.35 

0.05 

0.37 

0.66 

22 

13 

0.88 

0.29 

1.14 

0.37 

0.36 

0.10 

0.41 

0.52 

23 

13 

1.19 

0.17 

0.84 

0.12 

0.64 

0.12 

0.54 

0.72 

24 

27 

1.23 

0.21 

0.81 

0.14 

0.29 

0.08 

0.23 

0.29 

25 

8 

0.65 

0.07 

1.54 

0.16 

0.40 

0.03 

0.61 

0.96 

26 

22 

1.17 

0.10 

0.85 

0.07 

0.19 

0.07 

0.16 

0.25 

27 

27 

1.28 

0.08 

0.78 

0.05 

0.24 

0.07 

0.18 

0.31 

28 

24 

1.15 

0.17 

0.87 

0.13 

0.27 

0.07 

0.24 

0.40 

29 

31 

0.52 

0.25 

1.91 

0.91 

0.58 

0.09 

1.11 

0.55 

31 

14 

1.05 

0.08 

0.96 

0.07 

0.16 

0.11 

0.15 

0.07 

32 

28 

1.26 

0.62 

0.79 

0.39 

0.47 

0.16 

0.37 

0.21 

33 

20 

2.59 

0.35 

0.39 

0.05 

0.19 

0.07 

0.08 

0.27 

34 

10 

0.58 

0.54 

1.73 

1.64 

0.44 

0.21 

0.77 

0.28 

36 

5 

3.46 

1.78 

0.29 

0.15 

0.14 

0.44 

0.04 

0.00 

37 

19 

1.28 

0.10 

0.78 

0.06 

0.15 

0.03 

0.12 

0.53 

38 

15 

1.56 

0.11 

0.64 

0.04 

0.09 

0.03 

0.05 

0.39 

Sum/Mean 

720 

1.33 

0.03 

0.75 

0.00 

0.23 

0.01 

0.17 

0.34 


37 






Table 3-22. Values for k when P is Bound from Above by 1 


Subject 

N 

k 

SE 

R 2 

15 

24 

0.22 

0.02 

0.32 

22 

13 

0.32 

0.03 

0.55 

25 

8 

0.26 

0.03 

0.82 

29 

31 

0.28 

0.03 

0.55 

34 

10 

0.44 

0.05 

0.34 


38 







Seasonal Analysis 

Up to this point, the analyses have been carried out using values 
averaged across all visits during the year. However, some 
seasonal variation was noted in the values for the infiltration 
factor F^and the personal exposure factor F pex . In this section, 
we test whether we can obtain useful results on an individual 
home or person on a seasonal basis by regressing personal and 
indoor sulfur on outdoor sulfur concentrations by season. The 
advantage to this is that the seasonal variations, if any, will be 
observed; the disadvantage is that limiting the regression to a 
maximum of 7 values is likely to lead to wider variance and 
more uncertainty in estimating the two quantities of interest. 

Since air exchange rates are the only measured variable 
contributing to the infiltration factor (the others being the 
unmeasured P and k parameters), we compare the (hannonic) 
average air exchange by house and by season to the estimate of 
F m/ obtained by taking the average indoor/outdoor sulfur ratio by 
house and by season (Appendix Table A-l). Then we compare 
the estimate of F in f from the indoor/outdoor sulfur ratio to the 
corresponding estimate obtained by regressing indoor on outdoor 
sulfur and taking the slope of the regression as an estimate of F in f 
for each house and season (Appendix Table A-2). The estimates 
of F in f from the ratio are compared to the estimates from the 
slope in Figure 3-33. The R' value is 0.62, and the estimates 
from the regression slopes vary more widely (from 0 to >1) than 
the estimates from the indoor/outdoor ratios (0.2 to <1). 



Figure 3-33. Estimates for each home by season of the infiltration factor 
F,„Trom regressing indoor sulfur on outdoor sulfur (Slope) compared to 
estimates from the simple ratio of indoor sulfur to outdoor sulfur averaged 
over all visits in a season. 

Another way of comparing the estimates using the regressions to 
the estimates using the ratios is provided by Figures 3-34 and 3- 
35. Figure 3-34 presents the homes ordered by season and by the 
value of F,„/as determined from the ratio; Figure 3-35 presents 
the comparable results from the regressions. Comparing the two 
figures shows many cases in which the regression approach 
predicts low values for the infiltration factor (< 0.4), as well as a 
smaller number of cases in which the approach predicts very 


high values, including one impossible result (>1). This analysis 
confirms that the ratio provides the more stable estimate of the 
infiltration factor. 



Figure 3-34. Estimates of F inf by home from the indoor/outdoor sulfur 
ratio. 


1.2 


C 

I i 
• 

0) 

k- 

Ui 

a> 

C£ 

S 0.8 
o 

D 

o 



• 


\ 

• 

— 

• . 



■ 

o 


m m 



V 


<*> 

*■ 


O 

s 



♦ ** 



X 

*4 

°*\> 

V. 

*44 

*4. 


•X 

4 

«i_ 

- ■ 

4 




• 

% 

\ 

♦ Summer 



• Fall 

L- - - - 

♦ 

» Winter 

e 

■ 

• Spring 


J -,-♦-f- 


, _ 


0.6 


!§ 0.4 

3 

c n 
o 
o 

o 0.2 

to 


Figure 3-35. Estimates of F inf by home from regressions of indoor on 
outdoor sulfur. 


Multivariate Regressions 

All participants filled out a questionnaire on their household 
characteristics and daily activities. A total of 54 questionnaire 
variables were included in the dataset (see Table A-4 in the 
Appendix for the variable names and definitions). We carried out 
a series of multiple regressions on the personal, indoor, and 
outdoor fine particle and sulfur concentrations vs. these 
questionnaire responses to try to identify sources of increased 
exposure. 

Our first priority was to investigate the 54 independent variables 
for possible collinearity. This procedure is explained fully in the 
book Regression Diagnostics by Belsley, Kuh, and Welsch 
(1980). A factor matrix is prepared of the 54 variables and a 
“condition number’’ is calculated for each of the eigenvalues of 
the matrix. Belsley, Kuh, and Welsch (1980) recommend that if 


39 




























a condition number exceeds 30, then variables that have a heavy 
weight on that eigenvalue should be inspected and either 
combined or a way found to drop one of the variables. Four 
eigenvalues did in fact exceed a condition number of 30. The 
pairs of variables with the heaviest loadings on each eigenvalue 
were ROOMS/AREA, DRYER/DRYER VENT, 
DSTFACTORavg/C FUEL, and TEMPC/TEMPDELTA. Since 
AREA seemed the more precise factor, we dropped ROOMS. 
Since DRYER seemed the more fundamental variable, we 
dropped DRYER VENT. And since TEMPDELTA was a 
calculated variable including a number of imputed values, we 
dropped it. The choice between DSTFACTORavg and C FUEL 
was more difficult. The dust factor as estimated by the 
technician has previously (in the PTEAM Study, for example) 
been one of the strongest predictors of airborne particles. 
However, the cooking fuel (electric or gas) is the more 
fundamental variable, so we reluctantly dropped 
DSTFACTORavg from the list of variables. The final list 
contained 50 variables dealing with household characteristics, 
personal activities, and two measured quantities (air exchange 
and outdoor temperature). 

To include 50 variables in the regression and achieve an overall 
significance of p<0.05, the Bonferroni criterion was applied by 
dividing the chosen significance level by the number of 
variables. This gives us a p-value of 0.001 as the value required 
to achieve an overall significance of p<0.05 for the final model. 
In all of the following tables dealing with multiple regression 
results, the variables that meet the Bonferroni criterion for 
significance are listed in boldface type. 

The first step in the multiple regression approach was to carry 
out a stepwise regression (combined forward selection and 
backward elimination) on all 50 household characteristics and 
personal activities as well as one or two measured particulate 
matter or sulfur variables using Statistica 6 .1 software. A second 
regression was run including only those variables at the p < 0.05 
level. It is important to run the second regression on the smaller 
set of variables since this will include more cases (typically, for 
our data set, 20-70 additional records that were dropped because 
of the casewise elimination employed for all 50 independent 
variables.) To check our results, all of the initial 50-variable 
regressions were rerun on SAS statistical software using a 
standard backward elimination stepwise regression. Normally, 
SAS and Statistica identified the same significant variables, with 
minor differences in the slopes, intercepts, and R : values (the 
latter were always within 0-3% of each other). When a variable 
differed, both final versions were run one more time in Statistica, 
and the run with the higher R' value was selected. 

Although major collinearities were avoided, less strong ones 
remain. This could cause one or two of a collinear pair of 
variables to fail to register as significant in the first run involving 
50 variables; they will then have no chance to be considered in 
the final run picking out only the significant variables from the 


first run. In some situations, we inserted a variable that we 
suspected might be relevant after the “final'’ run and discovered 
that it too was significant, but never succeeded in changing the 
final R : value by more than 1% when adding individual 
variables, indicating that the most important variables in each 
regression were identified. 

In carrying out analyses of how household characteristics and 
personal activities affect indoor concentrations of particles, we 
need to carefully examine how the study design may have 
created unequal conditions among the homes. First, not all 
homes were visited for an equal number of seasons. Only 24 
homes were visited in all four seasons. Second, even within a 
season, only 3-6 homes were visited each week. Therefore 
different homes within any given season encountered different 
outdoor conditions. The homes were scrambled each season so 
that the same homes were not visited together in a given week 
over two seasons, so this should have evened out the outdoor 
conditions encountered to some extent, at least for the 24 homes 
visited all four seasons. However, for the entire group of 36 
homes, there was extensive variation in the outdoor PM 2.5 
concentrations, whether at the central site or outside the homes, 
ranging over a factor of two from the highest to the lowest 
(Figure 3-36). 



House 10 

Figure 3-36. Central-site and residential outdoor concentrations 
averaged over all visits to a home. 

Clearly, if homes at the high end of the outdoor concentrations 
happen by chance to have some characteristics substantially 
different from those at the low end, a simple regression of 
outdoor concentrations at the central site on these household 
characteristics may show significant results, even though the 
relationship cannot be causal (household characteristics cannot 
affect outdoor concentrations). In fact, some of these 
“impossible” relationships are observed. For example, a 
regression of the two central site monitors and the outdoor 
residential monitor on various household characteristics showed 
a number of artifactual relationships (Table 3-23). The building 
age and air exchange rates were among the strongest variables in 


40 

















Table 3-23. Multiple Regression of Outdoor Concentrations on Household Characteristics and Personal Activities 


Variable Slope Std.Err. p-level 


PM25out 

Intercept 

13.78 

1.13 

AGE 

0.13 

0.02 

Windopen 

3.94 

0.65 

airex 

-3.18 

0.57 

outdoor 

0.01 

0.01 

DRYER 

1.84 

0.87 

ambHI25 

Intercept 

10.58 

1.82 

airex 

-2.82 

0.52 

AGE 

0.11 

0.02 

C_FUEL 

2.95 

0.72 

Windopen 

2.45 

0.66 

FAN 

-3.66 

1.00 

MILDEWavg 

-2.77 

1.03 

spacehtr 

-4.63 

1.96 

VAC 

2.00 

1.02 

FRM 

Intercept 

10.60 

1.68 

airex 

-2.56 

0.49 

AGE 

0.10 

0.02 

FAN 

-3.73 

0.93 

C_FUEL 

2.46 

0.65 

Windopen 

1.97 

0.60 

MILDEWavg 

-2.74 

0.96 

spacehtr 

-4.27 

1.87 

VAC 

1.95 

0.95 


N R 2 (adj.) 

720 0.12 

0.000000 

0.000000 

0.000000 

0.000000 

0.003668 

0.034377 

743 0.10 

0.000000 

0.000000 

0.000004 

0.000042 

0.000202 

0.000264 

0.007241 

0.018484 

0.049494 

772 0.10 

0.000000 

0.000000 

0.000002 

0.000067 

0.000168 

0.001016 

0.004431 

0.022320 

0.039863 


41 









all three cases, along with open windows in two of the three 
cases, but it is likely that these variables simply represent 
differential outdoor air conditions encountered at the different 
times that the homes were monitored. The variables “explain” 
only 10 - 12 % of the variance of the outdoor monitors, but they 
represent a caution nonetheless in our interpretation of 
“significant” variables in all other of the multiple regressions we 
will consider. In particular, those regressions that include both 
an outdoor measurement and one or more of the variables AGE 
and airex (and windopen) will be putting the variables into 
“double jeopardy”, examining their effect both explicitly and 
implicitly as part of the outdoor particle measurement. Since the 
signs of airex and windopen are opposite, one might think that 
because they are correlated they end up with opposite signs, a 
common occurrence in regressions with correlated variables. 
Although it might seem that air exchange rates should be fairly 
well correlated with open windows, the Spearman correlation of 
airex and windopen was only 0.24. Nonetheless, the first 
regression in Table 3-23 was rerun twice, dropping airex from 
the first run and then restoring it and dropping windopen from 
the second run, to test whether some kind of cross-term 
relationship was occurring. However, in both cases the variable 
left in continued to be significant with the sign in the same 
direction as before, and the adjusted R‘ dropped in both cases, 
once to 0.10 and once to 0.08. Therefore, the “best” model for 
the residential outdoor PM 2.5 measurements continues to be the 
one listed first in Table 3-23. Again, for the definitions of the 
variables appearing in the next five tables, see Table A-4 in the 
Appendix. 

The indoor model is 

PM25in = a + /3 oul *PM25out + fi, *r, (3-13) 

where the x , are the 50 appropriate continuous/categorical 
variables. 

The model above can be repeated using FRM25 as the 
independent variable: 

P M2 5 in = a+ p out *FRM25 + /?, *x, (3-14) 

The point of using the FRM25 as the outdoor variable is that 
epidemiological studies are often limited to the central-site 
monitor. 

The results of these model runs are provided in Table 3-24. The 
first regression is a simple regression of indoor PM 2.5 on outdoor 
PM 2 . 5 . As can be seen, the relationship is particularly poor, with 
an R 2 of only 0.09. Since epidemiologists are often restricted to 

use of a fixed-site urban monitor, the regression is rerun using 

^ # 

the central-site FRM monitor. The R" remains low at 0.11. 
These results are consistent with other studies showing low 
cross-sectional relationships between indoor and outdoor PM 


levels. 

The R 2 value is increased to above 0.40 for multiple regressions 
including the questionnaire responses and either the residential 
outdoor monitor or the central-site monitor. In each case, the 
outdoor PM concentration has the greatest influence on the 
indoor PM (as judged by the p-value). Estimates for the outdoor 
contribution to indoor PM ranged from 48% (using residential 
outdoor PM) to 57% (using central site FRM). The difference in 
these estimates can be attributed to the consistently lower values 
returned by the FRM. Investigators have noted that the FRM 
may underestimate PM levels due to loss of volatile species such 
as nitrates. 

Burned food added about 12 pg/m 3 to the indoor concentration, 
while a nearby dirt road added 8.4 to 8.9 pg/m 3 , and use of an 
exhaust fan added about 5 pg/m 3 . It is likely that the use of the 
exhaust fan because of cooking or burned food added to the 
indoor concentration. 

Homes with persons who reported being near cigarette smoke 
also had higher indoor fine particle concentrations. The number 
of smokers increased indoor PM concentrations by 4-6 pg/m 3 , 
while time spent near smokers increased concentrations by 0.04 
pg/m 3 per minute exposed. 

Cooking increased PM 2.5 concentrations by about 0.03 pg/m 3 per 
minute. Homes with electric stoves had PM 2.5 concentrations 
6 . 6 - 6.8 pg/m 3 higher than homes with gas stoves, but it should be 
remembered that the homes with electric stoves also had higher 
outdoor PM 2.5 levels (see Table 3-23). Each additional person 
living in the household added 1.3 pg/m' to the daily average 
PM 2.5 concentrations. Vacuuming appeared capable of 
increasing daily average PM 2.5 concentrations by about 4 pg/m 3 , 
although the variable was only marginally significant (using the 
Bonferroni criterion) in one of the two regressions. Finally, 
homes with a clothes dryer were associated with a decrease of 
4.7 pg/m 3 in their daily average concentrations. The presence of 
a clothes dryer (nearly all of which were vented outdoors) 
increases air exchange since almost the same volume of air must 
enter the house to replace the heated air vented outdoors. 

The next series of two multiple regressions takes advantage of 
our ability to split indoor concentrations into indoor-generated 
and outdoor-generated fine particles (Table 3-25). 

The model for indoor-generated fine particles is the following: 

Incontrib = a + P*x-, (3-15) 

“Incontrib” is the contribution of indoor sources after subtracting 
the outdoor contribution determined by the indoor/outdoor sulfur 
ratio; therefore, this model does not include the outdoor PM 2 5 
variable. The model was initially run with this variable included 


42 



Table 3-24. Dependence of Indoor Fine Particle Concentrations on Household Characteristics and Personal Activities 


Variable 

Slope 

Std.Err. 

p-level N 

R 2 (adj.) 

PM25in 



775 

0.09 

Intercept 

8.47 

1.37 

0.000000 


PM25out 

0.54 

0.06 

0.000000 


PM25in 



760 

0.11 

Intercept 

7.21 

1.36 

0.000000 


FRM25 

0.67 

0.07 

0.000000 


PM25in 



762 

0.42 

Intercept 

-10.50 

2.91 

0.000325 


PM25out 

0.48 

0.05 

0.000000 


DIRT_RD 

8.89 

1.42 

0.000000 


Burning 

11.80 

1.93 

0.000000 


C_FUEL 

6.77 

1.36 

0.000001 


Numpeopl 

1.34 

0.29 

0.000003 


Exhstfan 

5.04 

1.11 

0.000007 


cooking 

0.03 

0.01 

0.000099 


numsmok 

6.33 

1.71 

0.000225 


DRYER 

-4.66 

1.40 

0.000912 


vacuum 

3.89 

1.20 

0.001220 


smoke 

0.04 

0.01 

0.001506 


Otherjndoor 

0.09 

0.03 

0.002624 


Unknown 

0.06 

0.02 

0.012012 


outdoor 

-0.02 

0.01 

0.016669 


PM25in 



747 

0.42 

Intercept 

-11.15 

2.91 

0.000138 


FRM25 

0.57 

0.06 

0.000000 


Burning 

11.94 

1.91 

0.000000 


DIRT_RD 

8.42 

1.40 

0.000000 


Exhstfan 

5.34 

1.11 

0.000002 


C_FUEL 

6.57 

1.36 

0.000002 


Numpeopl 

1.30 

0.28 

0.000006 


cooking 

0.03 

0.01 

0.000045 


vacuum 

4.04 

1.19 

0.000750 


DRYER 

-4.65 

1.40 

0.000909 


Otherjndoor 

0.10 

0.03 

0.001069 


Unknown 

0.06 

0.02 

0.004758 


smoke 

0.04 

0.01 

0.006304 


numsmok 

4.55 

1.76 

0.009757 


outdoor 

-0.02 

0.01 

0.021123 



43 







Table 3-25. Dependence of Indoor-Generated and Outdoor-Generated Particles on Household Characteristics and Personal Activities 


Variable 

Slope 

Std.Err. 

p-level 

N 

R 2 (adj.) 

Incontrib 




709 

0.37 

Intercept 

-12.03 

2.88 

0.000033 



Burning 

12.06 

1.97 

0.000000 



C_FUEL 

8.70 

1.45 

0.000000 



DIRT_RD 

8.01 

1.45 

0.000000 



airex 

-4.42 

0.83 

0.000000 



Numpeopl 

1.43 

0.30 

0.000003 



Exhstfan 

4.89 

1.15 

0.000024 



SWIN 

-4.56 

1.12 

0.000055 



cooking 

0.03 

0.01 

0.000064 



smoke 

0.05 

0.01 

0.000068 



Otherjndoor 

0.10 

0.03 

0.001016 



outdoor 

-0.03 

0.01 

0.002000 



Cleaning_1 

2.60 

1.00 

0.009668 



DRYER 

-3.64 

1.45 

0.012452 



Unknown 

0.05 

0.02 

0.024292 



Outcontin 




775 

0.69 

Intercept 

0.04 

0.31 

0.889273 



PM25out 

0.58 

0.01 

0.000000 



Outcontin 




686 

0.82 

Intercept 

-0.64 

0.54 

0.239662 



PM25out 

0.59 

0.01 

0.000000 



TempC 

-0.15 

0.02 

0.000000 



AGE 

0.06 

0.01 

0.000000 



Windopen 

1.68 

0.23 

0.000000 



cigsmokd 

0.35 

0.06 

0.000000 



AC 

0.39 

0.07 

0.000000 



windowail 

0.0022 

0.0006 

0.000069 



Numpeopl 

-0.18 

0.06 

0.002316 



FLRCOVav 

-0.02 

0.01 

0.002557 



vacuum 

0.64 

0.25 

0.011674 



airex 

0.55 

0.23 

0.014491 



fry 

-0.47 

0.21 

0.025024 




44 










as an independent variable to confirm that it had been fully 
accounted for. Results showed that it was not significant and 
could be omitted from the final model. 

The indoor-generated contribution ( Incontrib ) depends on many 
of the variables appearing in the Equation 3-13 and 3-14 
regressions for indoor PM 25 with about the same coefficient 
values, but does not depend on the outdoor concentration. The 
value of R' is 0.37 and again, burned food, cooking, use of 
electric stoves, nearness to a dirt road, use of the exhaust fan, the 
number of people living in the home, and exposure to ETS turn 
up as significant variables affecting indoor-generated particles. 
A new variable, the air exchange rate ( airex ), reduces indoor¬ 
generated concentrations as airex increases. 

The model for the outdoor contribution to indoor concentrations 
is the following: 

Outcontin = a + f3 olll *PM25oitt + /?, *jc, (3-16) 

“Outcontin” is the contribution of outdoor sources to indoor 
PM 2 .5 levels determined by the indoor/outdoor sulfur ratio. 

Results (Table 3-25) show that air exchange (airex, windopen, 
windowall) again significantly influences the results, this time 
increasing the outdoor contribution as their values increase. The 
additional variables increase R“ to 0.82, compared with an R" of 
0.69 for outdoor PM alone. The fact that air exchange variables 
appear as highly significant in these two regressions is important 
confirmation of our assumptions in using sulfur to identify 
indoor-generated and outdoor-generated PM. That is, if the 
assumption of no indoor sulfur source were violated in some 
homes, we would not be able to separate indoor-generated PM 
from outdoor-generated PM in those homes, and the relationship 
with air exchange would be weakened by the amount of the 
misclassification. Air exchange variables are not selected as 
significant in regressions of total indoor PM, which lumps both 
indoor-generated and outdoor-generated PM, because the 
influence of air exchange can work in two directions depending 
on whether indoor air PM is greater than or less than outdoor 
PM. But increased air exchange must increase outdoor-generated 
PM and it must decrease indoor-generated PM; therefore our 
finding of a strong effect in the expected directions when we 
analyze the indoor- and outdoor-generated concentrations 
separately must reflect a successful separation of the two 
sources. 

A model for indoor sulfur concentration is the following: 

S in = a + p out *S 0 ut + Pi % (3-17) 

Once again we first examine whether the outdoor concentration 
shows artifactual dependence on household variables by looking 
at outdoor sulfur (Som) alone (Table 3-26). Several artifacts 


appeared—air exchange, storm windows, time spent outdoors. 
However, these variables increased R from 0.59 using only 
PM 2 5 outdoors as the independent variable, to 0.65 using all 
significant variables, a relatively small increase. 

The results for indoor sulfur (S in ) suggest that there may be more 
sources of indoor sulfur than we have assumed above. For 
example, the number of pilot lights (pilot ) was a strongly 
significant variable, adding 133 ng/nv to the indoor sulfur 
concentration per each additional pilot light. Natural gas 
sometimes contains some sulfur, although if the level exceeds a 
certain amount it must be scrubbed. Smoking was also 
significant ( cigsmokd ), possibly due to the use of matches. The 
effect of open windows was also clear and expected. Homes 
with at least one open window ( windopen ) had increased indoor 
sulfur concentrations of 120 ng/irr. A quantitative measure 
(windowall) was an increase of 0.32 ng/nr for every inch-hour a 
window was open. Thus a window opened 6 inches wide for a 
day would result in an increase of 48 ng/nf S. Building age 
(AGE) was shown to be artifactually associated with outdoor 
PM; therefore, its appearance in this multiple regression may 
also be artifactual. Adding these variables to the simple 
regression of indoor on outdoor sulfur increased the R“ from 0.70 
to 0.83. 

We can estimate personal exposure from (1) outdoor 
concentrations alone; (2) indoor concentrations alone; (3) 
outdoor and indoor concentrations together; (4) outdoor 
concentrations and indoor concentrations together with 
household characteristics; and (5) outdoor concentrations alone 
together with household characteristics. These choices result in 


the following models of personal exposure: 

PM25pers = a + /.3 ou ,*PM25out (3-18) 

PM25pers = a + fi ollt *FRM25 (3-19) 

PM25pers = a + /?,„* PM25in (3-20) 

PM25pers = a + p ou , *PM25out +fi in *PM25in (3-21) 

PM25pers = a + fi otlt *PM25out + fi in * PM25in + (3-22) 

PM25pers = a + f} out *PM2 5 out + [5, *x, (3-23) 

PM25pers = a + / 3 oll *FRM25 + ft, *r ( (3-24) 


The results of the regressions are provided in Table 3-27. 
Outdoor PM 2 5 was a poor predictor of personal exposure, 
accounting for only 11-12% of the observed variance. Indoor 
PM 2 5 was a much better predictor, with an R" of 0.45. Adding 
the outdoor PM 2 5 variable to the indoor variable provided little 
improvement (R : = 0.47). Only one personal activity (grilling) 
was independently significant, increasing the R" to 0.49. 


45 



Table 3-26. Dependence of Outdoor Sulfur on Outdoor PM 2 5 and of Indoor Sulfur on Outdoor Sulfur and Household Characteristics and Personal 
Activities 


Variable 

Slope 

Std.Err. 

p-level 

N 

R 2 (adj.) 

Soot 




720 

0.59 

Intercept 

47.09 

66.44 

0.478737 



PM25out 

97.14 

3.01 

0.000000 



So of 




720 

0.65 

Intercept 

594.47 

85.83 

0.000000 



PM25out 

96.98 

2.85 

0.000000 



airex 

-354.67 

43.81 

0.000000 



S_WIN 

-216.48 

54.80 

0.000086 



outdoor 

-1.50 

0.41 

0.000240 



numsmok 

-209.72 

82.71 

0.011441 



AC 

-40.72 

18.05 

0.024394 



S ln 




720 

0.70 

Intercept 

151.44 

27.27 

0.000000 



So of 

0.49 

0.01 

0.000000 



Sin 




741 

0.83 

Intercept 

-19.76 

48.56 

0.684261 



Sout 

0.51 

0.01 

0.000000 



AGE 

7.91 

0.73 

0.000000 



PILOT 

133.01 

13.43 

0.000000 



windowall 

0.32 

0.05 

0.000000 



TempC 

-9.74 

1.59 

0.000000 



Windopen 

120.11 

23.85 

0.000001 



cigsmokd 

28.21 

5.66 

0.000001 



FAN 

-140.52 

30.52 

0.000005 



AC 

33.49 

7.29 

0.000005 



FLRCOVav 

-1.98 

0.51 

0.000104 



Numpeopl 

-20.14 

6.13 

0.001074 



fry 

-60.97 

20.94 

0.003705 



Unknown 

1.21 

0.47 

0.009623 



DIRT_RD 

81.66 

32.60 

0.012471 



vacuum 

61.71 

25.90 

0.017422 






46 










Table 3-27. Regressions of Personal Exposures to PM 2 5 on Household Characteristics and Personal Activities 


Variable 

Slope 

Std.Err. 

p-level N 

R 2 (adj.) 

PM25pers 



750 

0.10 

Intercept 

10.78 

1.56 

0.000000 


PM25out 

0.66 

0.07 

0.000000 


PM25pers 



734 

0.11 

Intercept 

10.44 

1.53 

0.000000 


FRM25 

0.73 

0.08 

0.000000 


PM25pers 





Intercept 

9.98 

0.69 

0.000000 727 

0.45 

PM25in 

0.67 

0.03 

0.000000 


PM25pers 



727 

0.47 

Intercept 

5.66 

1.12 

0.000001 


PM25in 

0.63 

0.03 

0.000000 


PM25out 

0.27 

0.05 

0.000001 


PM25pers 



727 

0.49 

Intercept 

4.32 

1.21 

0.000370 


PM25in 

0.60 

0.03 

0.000000 


PM25out 

0.26 

0.05 

0.000001 


grill 

11.54 

3.22 

0.000359 


cooking 

0.02 

0.01 

0.003309 


PM25pers 



737 

0.37 

Intercept 

-11.15 

3.63 

0.002239 


numsmok 

13.76 

1.54 

0.000000 


PM25out 

0.55 

0.06 

0.000000 


CANDLDUR 

0.05 

0.01 

0.000000 


Burning 

11.19 

2.23 

0.000001 


C_FUEL 

7.42 

1.56 

0.000003 


DIRT_RD 

6.05 

1.69 

0.000363 


Otherjndoor 

0.11 

0.03 

0.000800 


AC 

1.32 

0.40 

0.001039 


Exhstfan 

4.29 

1.36 

0.001652 


Cleaning_1 

3.46 

1.11 

0.001968 


cooking 

0.02 

0.01 

0.009918 


outdoor 

-0.02 

0.01 

0.013482 


PM25pers 



721 

0.39 

Intercept 

-11.81 

3.69 

0.001449 


FRM25 

0.60 

0.07 

0.000000 


numsmok 

13.31 

1.61 

0.000000 



47 








Table 3-27. Continued 


Variable 

Slope 

Std.Err. 

p-level 

CANDLDUR 

0.05 

0.01 

0.000000 

Burning 

11.71 

2.25 

0.000000 

C_FUEL 

7.44 

1.59 

0.000003 

A_C 

1.42 

0.41 

0.000492 

Other_indoor 

0.11 

0.03 

0.000553 

Exhstfan 

4.49 

1.37 

0.001106 

DIRTRD 

5.45 

1.70 

0.001407 

Cleaning_1 

3.52 

1.13 

0.001927 

cooking 

0.02 

0.01 

0.010664 

outdoor 

-0.02 

0.01 

0.015074 




R 2 (adj.) 


48 






When only outdoor PM 2.5 and the household characteristics were 
included in the regression, the most significant variable other 
than outdoor PM 25 was the number of smokers, adding between 
13.3 and 13.8 pg/m’ to the daily average personal exposure. The 
use of candles was highly significant, adding 0.05 gg/m 3 per 
minute burned to daily average personal exposure. Burned food 
added between 11.2 and 11.7 pg/nr' to personal exposure. 
Electric stoves increased particle concentrations by 7.4 pg/m 3 . 
Although these household characteristics and personal activities 
were useful in improving the R 2 for personal exposure, 
collectively they were not able to improve it as much as simply 
adding in the measured indoor PM 2.5 concentration (R 2 = 0.37- 
0.39 for the outdoor + household characteristics variables 
compared to 0.47 for the outdoor + indoor concentrations). 

By assuming that no sulfur is created indoors, we can estimate 
the contribution of personal activities to personal exposure 
(Perscontrib ) by multiplying the outdoor PM 2 .5 by the ratio of 
the personal sulfur concentration to the outdoor sulfur 
concentration, and subtracting that product from the observed 
personal exposure. As with the similarly defined indoor¬ 
generated particle contribution to total indoor concentrations, we 
can regress Perscontrib on the household characteristics and 
personal activities variables without including the outdoor 
concentration as an input, since we used it to determine the 
values of Perscontrib. Again, we checked to confirm that, when 
included, the outdoor concentration was not significant. The 
model is 

Perscontrib = a + ft, *x, (3-25) 

The focus on the non-outdoor part of personal exposure again 
showed that air exchange was an important variable, tending to 
decrease the personal contribution by 4.1 pg/m’ per unit change 
in the air exchange variable (Table 3-28). Smoking, burned 
food, and cooking fuel variables significantly influence personal 
exposure concentration. Candle burning increased personal 
exposure by 0.05 pg/m 3 per minute burned. 

The factors affecting the outdoor contribution to PM2.5 personal 
exposure were investigated using multiple regression (Table 3- 
29). Here the model is 

Outcontribpers = a + P ou ,*pm25out + /?, (3-26) 

The most influential factor, as expected, was the outdoor PM2.5 
concentration. This is so much stronger than all the rest of the 
factors put together (t-value of 45 compared to values <7 for the 
other variables) that it can be considered the single variable 
driving personal exposure to particles of outdoor origin. The 
next strongest factor (AGE) may be an artifact due to the 
unbalanced design of the study, in which not all homes were 
monitored on the same day. The next three factors have to do 
with air exchange, which depends on window opening behavior 


(win c/open, window all) and on indoor-outdoor temperature 
differences (roughly approximated by TempC). The increased air 
exchange rates due to increases in these variables brings more 
outdoor particles indoors, and thus affects personal exposure to 
the extent the person is at home. Note that the R 2 of 0.82 for this 
model of the influence of outdoor particles on personal exposure 
precisely matches that for the influence of outdoor particles on 
indoor particle concentrations (last part of Table 3-25). 

Finally, personal exposure to sulfur was regressed against 
outdoor sulfur and the household characteristics, using the 


following set of models: 

Spers = a + /?,„ *Sin (3-27) 

Spers = a+ fi ou ,*Sout + Pm*Sin (3-28) 

Spers = a + fi otl ,*Sout (3-29) 

Spers - a + fi ou ,*Sout + fi m *Sin + b*Xj (3-30) 
Spers = a + p ou , *So ut + P, *x, (3-31) 


These correspond to having greater or less information about the 
outdoor and indoor sulfur concentrations and the questionnaire 
variables. 

In general, very high values of R" are achieved (Table 3-30). 
The worst case is having only the outdoor sulfur concentration, 
and that alone explains 0.78 of the variance in personal exposure 
to sulfur. If the indoor sulfur were known alone, the R 2 could be 
improved to 0.88. Using both measured indoor and outdoor 
concentrations to estimate personal exposure increases R 2 to 
0.91. Other influential variables included time spent near a 
smoker and the number of pilot lights in the house, both possible 
sources of sulfur as mentioned above. 

Variables Affecting Air Exchange and the 
Infiltration Factor 

Our primary focus has been on documenting the influence of 
outdoor fine particles on indoor concentrations and personal 
exposures. Two of the most influential factors are air exchange 
and the infiltration factor. 

Variables Affecting Air Exchange 

Previous work (Howard-Reed et al., 2002; Wallace et al., 2002) 
identified window opening and the absolute indoor-outdoor 
temperature difference as two main variables affecting air 
exchange. To create a variable approximating the absolute 
indoor-outdoor temperature difference, we took the absolute 
value of the difference between the outdoor temperature 
(TempC) and a typical indoor temperature of 72 F (22.2 C). The 
new variable was called AbsTempDif. 


49 



Table 3-28. Regression of the Non-ambient-related Contribution ( Perscontrib ) to Personal PM 25 Exposure 


Variable 

Slope 

Std.Err. 

p-level N 

R 2 (adj.) 

Perscontrib 



677 

0.30 

Intercept 

-9.68 

3.55 

0.006489 


numsmok 

11.45 

1.57 

0.000000 


CANDLDUR 

0.05 

0.01 

0.000000 


C_FUEL 

8.44 

1.62 

0.000000 


Burning 

10.83 

2.25 

0.000002 


airex 

-4.12 

0.97 

0.000024 


Cleaning_1 

4.26 

1.14 

0.000194 


DIRT_RD 

5.85 

1.69 

0.000569 


Exhstfan 

4.61 

1.38 

0.000850 


outdoor 

-0.03 

0.01 

0.001324 


AC 

1.31 

0.41 

0.001585 


Otherjndoor 

0.09 

0.03 

0.005808 


cooking 

0.02 

0.01 

0.022302 



X' 


50 









Table 3-29. Regression of the Ambient-Related Contribution to Personal PM 2 5 Exposure 



B 

SE 

t(691) 

p-level 

N 

R 2 (adj.) 

Intercept 

-0.952 

0.588 

-1.6 

0.105780 

710 

0.82 

PM25out 

0.521 

0.012 

45.2 

0.000000 



AGE 

0.044 

0.007 

6.7 

0.000000 



windowall 

0.003 

0.000 

6.4 

0.000000 



Windopen 

1.078 

0.211 

5.1 

0.000000 



TempC 

-0.062 

0.012 

-5.0 

0.000001 



cigsmokd 

0.221 

0.056 

3.9 

0.000101 



MILDEWavg 

1.113 

0.296 

3.8 

0.000187 



smoke 

0.009 

0.002 

3.6 

0.000288 



cooking 

0.005 

0.001 

3.6 

0.000311 



AC 

0.234 

0.068 

3.5 

0.000593 



DIRT_RD 

0.959 

0.295 

3.3 

0.001196 



T ravel 

0.004 

0.002 

2.9 

0.004144 



Pets 

0.754 

0.298 

2.5 

0.011484 



dust 

-0.688 

0.271 

-2.5 

0.011302 



FLRCOVav 

-0.012 

0.004 

-2.8 

0.005730 



Grooming 

-0.006 

0.002 

-3.0 

0.002672 



AREA 

-0.001 

0.000 

-3.2 

0.001409 




51 





Table 3-30. Regressions of Personal Exposure to Sulfur on Indoor and Outdoor Concentrations and Questionnaire Variables 


Variable 

Slope 

Std.Err. 

p-level 

N 

R 2 (adj.) 

Spers 




727 

0.88 

Intercept 

42.50 

15.67 

0.006860 



Sin 

0.91 

0.01 

0.000000 



Spers 




727 

0.91 

Intercept 

-12.71 

14.32 

0.374879 



Sin 

0.66 

0.02 

0.000000 



Sout 

0.17 

0.01 

0.000000 



Spers 




750 

0.78 

Intercept 

89.52 

21.54 

0.000036 



Sout 

0.49 

0.01 

0.000000 



Spers 




722 

0.93 

Intercept 

-1.59 

31.71 

0.959998 



Sin 

0.65 

0.02 

0.000000 



Sout 

0.17 

0.01 

0.000000 



DIRTRD 

117.68 

18.99 

0.000000 



smoke 

0.87 

0.16 

0.000000 



windowall 

0.14 

0.03 

0.000006 



MILDEWavg 

81.04 

20.08 

0.000060 



C_FUEL 

-55.33 

15.33 

0.000327 



Grooming 

-0.45 

0.14 

0.001283 



outdoor 

0.34 

0.11 

0.002197 



Otherjoc 

0.22 

0.08 

0.010413 



TempC 

2.46 

1.07 

0.022455 



Spers 




715 

0.85 

Intercept 

28.25 

58.70 

0.630470 



Sout 

0.50 

0.01 

0.000000 



PILOT 

123.65 

13.27 

0.000000 



windowall 

0.36 

0.05 

0.000000 



AGE 

3.63 

0.74 

0.000001 



AREA 

-0.10 

0.02 

0.000040 



smoke 

1.03 

0.26 

0.000105 



DIRT_RD 

111.72 

31.23 

0.000372 



FLRCOVav 

-1.62 

0.48 

0.000862 



Windopen 

72.43 

22.40 

0.001280 



TempC 

-4.59 

1.51 

0.002480 



MILDEWavg 

92.35 

30.90 

0.002903 



Travel 

0.47 

0.16 

0.003808 



BUSYRD 

70.43 

24.83 

0.004686 



FAN 

-76.58 

29.86 

0.010533 



cooking 

0.33 

0.14 

0.017800 



cigsmokd 

13.98 

.6.01 

0.020369 



sweep 

-45.29 

21.61 

0.036424 




52 










Because we saw increased air exchange rates in summer, we 
considered using Season as a separate independent variable. The 
reasoning here is that perhaps people would tend to keep the 
windows closed and the air conditioner running in Summer even 
on a relatively cool day, whereas on a day with the identical 
temperature in another season they might not bother turning on 
the air conditioner. Thus, both temperature and season could 
have separate effects. First, Season was coded by average 
temperature, using a 3-point scale (Winter = 1, Fall and Spring = 
2, Summer = 3); however, a regression including either season or 
TempC showed that TempC produced slightly higher R 2 . It was 
then thought that a 3-point scale might be too restrictive, so a 5- 
point scale was created by month (e.g., Jan-Feb = 1, July-August 
=5). There were only 10 months, with March and December 
missing. The new variable was called MonthbyTemp. This time 
when the regression was run putting in either TempC or 
MonthbyTemp , the R using the MonthbyTemp variable was 
slightly higher than that using TempC (Table 3-31). 

The two strongest variables are in fact those that we already 
know from previous studies: the window opening width and the 
absolute indoor-outdoor temperature difference. The latter has a 
coefficient of 0.04 ach/°C, which agrees well with the estimate of 
0.02 ach/°C in Wallace et al. (2002). The next strongest variable 
is the number of persons in the home. This variable, as well as 
the pets variable, was included on the questionnaire because of 
results from past studies (e.g., Thatcher and Layton, 1995) 
indicating that people and pets can increase particle levels due to 
resuspension and going outdoors more often, which will bring in 
outdoor particles when the door is open. The pets variable also 
appears, although just missing significance. The next strongest 
variable is age of the home—previous studies have noted that 
older homes are constructed more loosely. The use of the 
exhaust fan tends to increase air exchange, as noted in a previous 
study (Wallace et al., 2002). Another variable is the 
MonthbyTemp variable, reflecting the increased use of air 
conditioners (and therefore closed windows) in the summer. The 
presence of a clothes dryer (nearly all of which were vented 
outdoors) increases air exchange since almost the same volume 
of air must enter the house to replace the heated air vented 
outdoors. We have no explanation for the appearance here of the 
VAC and FLRCOVav variables, although their effect on the total 
R : value of the model is very small. 

Variables Affecting the Infiltration Factor 

The multiple regression on the indoor/outdoor sulfur ratio was 
run on all variables, including airex (Table 3-32). However, the 
five strongest variables contributing to this ratio can all be seen 
to be variables already contributing to the air exchange rate. 
Therefore these variables are, in a sense, being double-counted. 
They are obscuring and weakening the actual relationship with 
the air exchange rate. Therefore the regression was run again 
including all variables except those already found to contribute 
to the air exchange rate (Table 3-33). The final model shows 


that the air exchange rate (together with the windows open 
variable) is the strongest variable affecting the sulfur 
indoor/outdoor ratio. The air exchange rate is the only one of our 
variables that explicitly appears in the equation for the 
infiltration factor: ( Pa/(a+k)). The three remaining variables 
provide very little contribution (about 3%) to the total R' of the 
model. One of these, A/C, appears to have the wrong sign, since 
presence of an air conditioner would be expected to reduce the 
infiltration factor. However, this variable ranges from 1 to 5 air 
conditioners and therefore is measuring the falloff in efficiency 
as one goes from central air conditioning to multiple window air 
conditioning. 


53 



Table 3-31. Variables Affecting Air Exchange Rate 


Variable 

Slope 

Std.Err. 

t(711) 

p-level 

N 

R 2 (adj.) 

airex 





744 

0.51 

Intercept 

0.5156 

0.1355 

3.8 

0.00015 



windowall 

0.0010 

0.0001 

13.2 

0.000000000000 



ABSTempDIF 

0.0434 

0.0040 

10.8 

0.000000000000 



Numpeopl 

0.0693 

0.0094 

7.3 

0.000000000001 



AGE 

0.0079 

0.0013 

6.3 

0.00000000062 



VAC 

-0.3079 

0.0496 

-6.2 

0.00000000093 



FLRCOVav 

-0.0043 

0.0008 

-5.1 

0.00000044 



DRYER 

0.2071 

0.0458 

4.5 

0.0000073 



MILDEWavg 

-0.2406 

0.0544 

-4.4 

0.000011 



Monthbytemp 

-0.0673 

0.0153 

-4.4 

0.000012 



Mealsckd 

-0.1488 

0.0390 

-3.8 

0.00014 



Exhstfan 

0.1375 

0.0410 

3.4 

0.00085 



Unknown 

0.0025 

0.0008 

3.3 

0.0012 



Pets 

0.1586 

0.0523 

3.0 

0.0025 



Table3- 32. Variables Affecting Indoor/Outdoor Sulfur Ratio 

Variable 

Slope 

Std.Err. 

t(638) 

p-level 

N 

R 2 (adj.) 

Sinout 





659 

0.46 

Intercept 

0.68439 

0.02916 

23.5 

0.00000000000000 



Monthbytemp 

-0.03656 

0.00343 

-10.7 

0.00000000000000 



AGE 

0.00299 

0.00037 

8.1 

0.00000000000000 



airex 

0.06455 

0.00925 

7.0 

0.000000000008 



Windopen 

0.06168 

0.01027 

6.0 

0.0000000031 



windowall 

0.00014 

0.00002 

5.9 

0.0000000062 



C_FUEL 

-0.06117 

0.01047 

-5.8 

0.0000000083 



DIRTRD 

0.06170 

0.01362 

4.5 

0.0000070 



VAC 

-0.05484 

0.01323 

-4.1 

0.000039 



DUSTY_RD 

-0.04057 

0.01203 

-3.4 

0.00079 



Table 3-33. Variables Affecting Indoor/Outdoor Sulfur Ratio: 

Reduced Model 



Variable 

Slope Std.Err. 

t(699) 

p-level N 

R 2 (adj.) 


Sinout 




704 

0.43 


Intercept 

0.409 0.010 

40.3 

0.000000000000 



airex 

0.121 0.008 

15.1 

0.000000000000 



Windopen 

0.090 0.010 

9.2 

0.000000000000 



A_C 

0.015 0.003 

4.3 

0.000018 



numsmok 

0.055 0.015 

3.7 

0.00021 




BUSY RD 

0.036 0.011 

3.2 

0.0013 





54 























Chapter 4 
Discussion 


To reach our primary goal of estimating the contribution of 
outdoor PM 2 5 to personal exposures, we set an intermediate goal 
of estimating the contribution of outdoor particle concentrations 
to indoor particle concentrations. Since personal exposures 
depend heavily on indoor concentrations, this may be a good first 
approximation. We calculated the contribution of outdoor 
particles by multiplying the outdoor concentration by the ratio of 
the indoor to outdoor sulfur concentrations: 


where C ino is the concentration indoors of particles infiltrating 
from outdoors, and where the SJS out term is an estimate of F in f. 
We also calculated F in f using a combination of the 
indoor/outdoor sulfur ratios and the air exchange measurements 
and found excellent agreement (R~ = 0.96-0.99), indicating that 
the estimates of F,„/for individual homes were quite stable. This 
is one of the first studies to calculate infiltration factors and 
outdoor exposure factors for individual homes and persons, 
respectively, and is also one of the first to carry out the 
regressions of that portion of personal exposure due to particles 
of outdoor origin on outdoor concentrations. 

Limitations of the Regressions and 
Influence of Assumptions 

When we run a regression of C ino on C oul , we are in fact 
regressing a term on a portion of itself: 

FjnfCou, = A C ou , + B (4-2) 

The infiltration factor does not have a high degree of day-to-day 
variability: F in f= 0.59 (0.16 SD) for all homes, and an even 
smaller standard deviation within homes. Therefore our 
regression is not very different from regressing a term on a 
constant fraction of itself, which will result in an R" of 1. This is 
a result that is forced by our basic assumptions - no indoor 
sources of sulfur, no coagulation, condensation, particle-gas 


conversion, or indoor chemistry, negligible transient terms, 
instantaneous perfect mixing, penetration and deposition 
characteristics of PM 2 5 identical to those of sulfur - leading to a 
simple linear relation between the outdoor concentration and the 
fraction of outdoor air particles infiltrating the house. The 
regression on outdoor air measured at all the homes resulted in 
an R 2 of 0.71, a value that is certainly higher than reality due to 
our assumptions. The individual regressions on 36 homes gave 
even higher values of R* (median 0.77). When the regressions 
were run on the outdoor PM 2 5 concentrations measured by the 
FRM at the central site, the overall R value was lowered to 0.60 
(median 0.73), but with the same caveat that our assumptions 
force a high correlation and therefore these estimates are not be 
considered best estimates, but rather upper bounds. 

A regression of personal PM 2 5 on outdoor PM 2 5 concentrations 
provides an estimate of F pex of 0.59 + 0.01 SE, with one 
influential outlier removed. With the outlier included, the 
estimate was 0.64 + 0.02 SE. The intercept with the outlier 
excluded (12.3 + 1.4 pg/nf) is an estimate of E no , the exposure 
due to non-outdoor sources. The difference between the indoor 
source contributions estimated at 7.7 pg/m 3 in Table 3-5 and the 
non-outdoor source contribution is sometimes attributed to the 
“personal cloud,” which in this case equals 4.6 + 2.0 pg/nf. 

The similarity of the slopes for the personal vs. outdoor and 
indoor vs. outdoor PM 2 5 regressions (0.59 compared to 0.60) 
suggests that the time spent indoors drives the relationship 
between personal exposure and outdoor concentrations. That is, 
the infiltration factor F m f, which governs the reduction of particle 
concentrations as they enter a house, is very similar to the 
outdoor exposure factor F pex , which governs the reduction in 
outdoor concentrations contributing to personal exposure. 

A major assumption has been that fine particles in general will 
behave like sulfur in terms of penetration and deposition. How 
good is this assumption? Sulfur is found primarily in the fine 
fraction, and within that fraction it typically has diameters <0.5 


55 



|im, smaller than most other elements. From theoretical and 
experimental studies, deposition velocities appear to reach a 
minimum around diameters of 0.1-0.2 pm (Lai and Nazaroff, 
2000). Deposition rates for sulfur and other elements were 
estimated in the PTE AM study (Ozkaynak et al, 1996). The rate 
for sulfur was 0.16 h ' 1 (0.02 SE). Rates for other elements were 
generally higher (e.g., iron, 0.70 h " 1 (0.30 SE)). Therefore sulfur 
will stay elevated indoors longer than most other elements, and 
possibly longer than most of the remainder of the fine particle 
mass. This leads to overestimates of the contribution of outdoor 
particles to indoor PM 2 . 5 . Evidence for the amount of the 
overestimate is provided by the results for iron concentrations. 
The indoor/outdoor ratio for iron was 0.38 (0.18 SD; N = 766). 
This was the lowest ratio of all the elements. For Si it was 0.58 
(N = 766); for Mn it was 0.52 (N = 409). These were elements 
that appeared to have few indoor sources, judging from their 
high indoor-outdoor product-moment correlations (0.81 for Fe, 
0.74 for Si, N = 762 samples for each; 0.58 for Mn, N = 460 
samples; these correlations were almost as high as the value of 
0.85 for sulfur). For the other elements, the indoor/outdoor 
correlations were lower and the indoor/outdoor ratios higher due 
to indoor sources, and cannot be used to help estimate the 
amount of the overestimate due to using the sulfur ratios. Iron 
itself makes up only a small fraction of PM 2.5 mass, but it could 
be a marker for the behavior of a larger fraction. 

Sulfur accounted for 20% of indoor PM 2 . 5 , assuming it is in the 
form of ammonium sulfate. If the remaining 80% of the indoor 
particle mass behaved more like iron, outdoor air particles would 
account for only 42% of indoor air PM 2 . 5 , rather than 58% as was 
found using sulfur alone. However, it is unlikely that the 
remainder of the indoor air particles behave more like iron, 
because the RCS model regression of indoor PM 2 5 on outdoor 
PM 2.5 (Figure 3-13) resulted in a slope very close to the slope 
predicted by the sulfur indoor-outdoor ratio. These 
considerations indicate that the overestimate of the outdoor 
contribution due to relying on the sulfur ratios is not a major one 
and does not affect the relative order of the homes in terms of the 
fraction of indoor PM 2.5 produced by outdoor particles. 

Based on previous studies, we attempted to use the particle 
measurements to estimate an infiltration factor for each home; 
however, we found poor agreement with the estimates in this 
study using the sulfur ratio. Most other studies of personal 
exposure and/or indoor concentrations of fine particles have 
fewer measurements per home than this study. Therefore our 
results suggest that particle mass measurements alone cannot 
provide reliable estimates of F in f. 

We estimated the outdoor exposure factor F pex using two 
methods: personal/outdoor sulfur ratios and a model using time 
indoors and outdoors. The model consistently overestimated the 
outdoor factor. We speculate that this is because people spend 
time in unmonitored environments, and our assumption that 
exposures are the same in those environments as in the 


monitored home environment may be incorrect. We found that 
estimating personal exposure using only the indoor/outdoor 
sulfur ratio (F in J) gave better results than estimates using the 
modeled outdoor exposure factor F pex based on time spent 
indoors and outdoors. 

Although several different methods were employed in making 
these estimates, the methods ultimately depended on the sulfur 
measurements only. Because of the way P and k are related in 
the equation for F in f, an infinite number of solutions are available 
for any given infiltration factor. The solution surface for P and k 
is very flat (meaning a very wide error associated with all point 
estimates); therefore a slight error in any measurement can lead 
to a very large error in the estimation of P and k. For example, 
each of the slightly different methods produced four or five 
physically impossible estimates for P. Although bounds can be 
established to prevent P from exceeding unity, the existence of 
these nonphysical solutions suggests that measurement errors or 
violations of our assumptions may have caused large errors in 
estimating these parameters. Since we have no independent 
methods of estimating these parameters, we are unable to 
validate our estimates of P and k for individual homes. The 
estimates of P and k that were significantly different from zero 
included about 24 of the 36 homes. The median value for the 
penetration coefficient P was 0.81 (interquartile range 0.66 to 
0.90). The median for the deposition rate k was 0.24 h ' 1 
(interquartile range 0.12 to 0.35 h 1 ). These estimates are similar 
to those obtained in some other studies (Liu et al., 2003; Wallace 
et al., 2002; Thatcher et al., 2003). 

Because not all homes were done in the same time periods, there 
were certain unavoidable differences in the outdoor particle 
concentrations encountered at the time they were monitored. We 
attempted to identify possible artifacts (significant associations 
with no possible causal explanations) by regressing central-site 
and residential outdoor concentrations vs. the questionnaire 
variables. We did find some artifacts, and therefore caution that 
some apparently significant relationships may be due to these 
temporal variations rather than a true causal relationship. This 
may be particularly true for certain unvarying household 
characteristics such as building age, location near a road, and 
type of cooking fuel (gas vs. electric) because these unvarying 
characteristics enter the regressions as a block of entries repeated 
up to 28 times per house. Therefore their number of degrees of 
freedom is greatly reduced and any variation due to temporal 
heterogeneity will be multiplied by this repeated appearance. 
This caution extends to the variables AGE, DIRT ROAD, 
C FUEL (cooking fuel), S_WIN (storm windows), VAC, and 
AREA among others. 

Nonetheless, regressions of indoor concentrations and personal 
exposures showed greatly improved R 2 results when 
questionnaire variables were added to the outdoor 
concentrations. The improvement was from R 2 about 0.1 to R 2 
about 0.4, for both indoor and personal estimates. Among the 


56 



significant contributors to indoor and personal exposure were 
smokers in the household, cooking, and number of people in a 
household, all variables that have previously been found to 
contribute to indoor PM concentrations (Ozkaynak et al., 1996b; 
Wallace et al., 2004b). Some variables not previously identified 
as contributing to indoor or personal exposures were burning 
food, duration of candle use, number of pilot lights, use of an 
exhaust fan, proximity to a dirt road, and presence of electric 
stoves (compared to gas stoves). The latter two variables are to 
be treated with caution since they are unvarying household 
characteristics and therefore may have been subject to temporal 
unevenness of sampling. Vacuuming was another variable that 
achieved significance at times. The presence of a clothes dryer 
was significantly associated with reduced indoor concentrations 
and personal exposure. 

An important result was the appearance of the air exchange 
variables (including windows open or closed) in the regressions 
on estimated concentrations of indoor-generated and outdoor¬ 
generated particles. The fact that these variables appeared in the 
expected directions whereas they did not appear when regressing 
on the mixture of both indoor-generated and outdoor-generated 
particles is confirmation that our division into the two 
components using the sulfur ratios was successful. 

We found several variables that were important in affecting air 
exchange rates. Opening windows was the single most important 
variable, as has been shown in previous studies (Howard-Reed et 
al., 2002). The indoor-outdoor temperature difference (which 
affects the indoor-outdoor pressure difference and therefore the 
driving force for air exchange) was the next most powerful 
variable. The coefficient for this variable (0.04 ach/°C) was of 
similar magnitude to that found by Wallace et al. (2002). The 
number of people in the home and the presence of pets, both 
significant variables in this study but not often found in other 
studies, contribute to increased air exchange through the 
increased number of times going in and out of the house. The use 
of a kitchen exhaust fan was associated with an increase of 0.14 
ach. This also compares well with the finding of a 0.8 ach 
increase associated with use of an attic exhaust fan (Wallace et 
al., 2002), given the much longer periods that an attic exhaust 
fan may run compared to a kitchen exhaust fan. 

The single strongest variable affecting the infiltration factor F,„f 
is the air exchange rate, as can be seen from the equation for F m f 
and the observed relationship shown in Figure 3-27. All other 
variables entering our analysis are either highly correlated with 
air exchange rate or appear to be artifacts arising from the 
unequal monitoring of homes on different days. 


57 



Chapter 5 
Conclusions 


The sulfur measurements showed excellent agreement between 

the personal samplers and the fixed indoor-outdoor Harvard 

Impactors. More than 160 co-located measurements both 

indoors and outdoors resulted in slopes insignificantly different 
t ... . ,0 
from 1 and intercepts insignificantly different from zero, with R~ 

values of 0.97 for both indoor and outdoor locations. 

Uncertainties of individual sulfur measurements were estimated 

at 8%. 

The data appear to be consistent with the hypothesis that there 
are few indoor sources of sulfur. Only about 1% of 
measurements showed higher levels indoors than out. Although 
nearly all regressions of indoor sulfur vs. outdoor sulfur gave 
positive intercepts, these intercepts were often relatively small 
and could be due to measurement error. Therefore we accepted 
the indoor/outdoor sulfur ratio as our best estimate of F m f for 

pm 25 . 

Estimates of F in f averaged over all seasons varied over a large 
range by household (0.26-0.87), but half of the homes were 
within 20% of the median value of 0.59. The infiltration factor 
was significantly lower for many homes in the summer season, 
presumably due to the closed windows and increased 
recirculation and filtration of air associated with the use of air 
conditioners. Evidence from measurements of iron and silicon 
suggested that the estimates of the infiltration factor using the 
sulfur indoor/outdoor ratio are likely to overestimate the 
influence of outdoor air due to the low deposition velocity of 
sulfur compared to other likely constituents of PM 2 5 . However, 
other considerations lead us to think that the overestimate is not 
large; also, it would not affect the relative ranking of the homes 
with respect to the outdoor contributions to indoor PM 2 5 . 

In general, the outdoor exposure factor F pex was very similar to 
F in f. This is expected, since persons spend most of their time 
indoors, but the very close agreement is also an indication that 
the sulfur measurements were reproducible and agreed well even 


though the personal monitor collected 10 times less material than 
the indoor/outdoor monitors. 

Fpex was usually smaller by a few percent than F mf , although a 
simplistic application of time-activity budgets indicates that it 
should be larger by about 6 or 7% for persons spending a typical 
amount of time outdoors and in vehicles. This may be due to 
time spent in unmonitored indoor locations (e.g., office 
buildings, department stores) that have mechanical ventilation, 
recirculation, and filtration, thus lowering exposure to sulfur 
while in those locations. The fact that F pex was slightly larger 
than F in f during the summer season, when many homes were 
closed and using recirculated and filtered air, supports this 
hypothesis. We conclude that the model advocated in earlier 
publications for determining F pex by using time-activity budgets 
is not useful for studies that do not measure indoor 
concentrations in schools, workplaces, shopping malls, and other 
locations where persons spend substantial amounts of time. The 
preferred way to estimate F pex is by using personal sulfur 
measurements, but lacking those, it may be just as useful to use 
F,„/as the best estimate of F pex . 

Regressions of indoor air concentrations due to particles of 
outdoor origin vs. outdoor concentrations had generally high R 2 
values, as did regressions of personal exposure to particles of 
outdoor origin vs. outdoor concentrations. However, this result 
is partially due to the many assumptions in our approach leading 
to a simple linear relation between indoor concentrations of 
particles of outdoor origin and outdoor concentrations. 
Therefore these estimates should not be treated as best estimates, 
but rather as upper bounds to the actual amount of variance in 
personal exposure to PM 2 5 of outdoor origin that can be 
explained by outdoor measurements at the central site. 

We investigated whether particle measurements alone could be 
used to estimate the infiltration factor. However, comparisons 
with the sulfur ratio method indicated that particle measurements 


58 



alone cannot be used to give reliable estimates of F mf . 

The two unmeasured parameters contributing to F mf (P and k) 
were estimated using both linear and nonlinear approaches. The 
median value for the penetration coefficient P was 0.81 
(interquartile range 0.66 to 0.90). The median for the deposition 
rate k was 0.24 h" 1 (interquartile range 0.12 to 0.35 h' 1 ). Recall 
that these values of k and P are for particles in the same size 
range as sulfur particles. However, we conclude that the 
unphysical values obtained for P in some cases casts doubt on all 
the estimates of P and k for individual homes; we are unable to 
validate these estimates. 

Air exchange rates were found to depend primarily on opening 
windows and on the absolute indoor-outdoor temperature 
difference. Other contributors included use of a kitchen exhaust 
fan, presence of a vented clothes dryer, and number of persons in 
a household. The infiltration factor was primarily dependent on 
air exchange, as expected from the basic equilibrium equation. 

Regressions of indoor concentrations and personal exposures 
showed considerably improved R 2 estimates by consideration of 
questionnaire variables. In particular, smoking, cooking, number 
of persons in a household, and burned food were important 
contributors. Air exchange rates were also very important 
variables, but only after the total indoor particle concentration 
had been split into indoor-generated and outdoor-generated 
portions. The strong effect of air exchange (increasing outdoor¬ 
generated particle concentrations and decreasing indoor¬ 
generated particle concentrations) was an important confirmation 
of the success of our efforts to resolve these two contributors to 
total indoor particle levels. 


59 



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of the Air and Waste Management Association 50(7): 1184- 
1198. 

Sarnat, J. A., Schwartz, J., Catalano, P. J., and Suh, H. H. 

(2001). Gaseous pollutants in particulate matter 
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Health Perspectives 109:1053-1061. 

Sarnat, J. A., Long, C. M., Koutrakis, P., Coull, B. A., Schwartz, 
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63 



Appendix 


In this Appendix we document the seasonal variation of the 
infiltration factor F,„/and the ambient exposure factor F pex . Table 
A-l presents the seasonal average of F in f and the seasonal 
harmonic average of the air exchange rate for each home. 

Table A-2 compares the seasonal average indoor/outdoor ratio 
for sulfur to the slopes obtained when regressing indoor on 
outdoor sulfur. The slope is usually smaller than the ratio, 
averaging 91% of its value, and also has a wider range, with a 
standard deviation almost twice that of the ratio (0.26 compared 
to 0.14). This is to be expected, since measurement error causes 
lower slopes in regressions, and the regressions are based on a 
maximum of 7 values (with a few exceptions), and usually only 
5, 6, or 7; this leads to more variability than is desirable. 

Table A-3 compares the personal/outdoor ratios (F pex ) averaged 
over one season with the slopes determined from regressions of 
personal on outdoor sulfur. As with the F m /values in Table A-2, 
the slopes are lower than the ratios and once again the standard 
deviations are nearly twice as high (0.19 compared to 0.11). 

The results of regressing the outdoor contribution to personal 
exposures (by season) on the outdoor monitors are provided in 
Figures A-l to A-3, which provide boxplots of the adjusted R~ 
values. Note that the definition of the adjusted R~ parameter 
allows negative values. The range of these seasonally calculated 
R : values is quite a bit larger than the range of the year-round R : 
and suggests that the increased variability due to the smaller 
number of observations in each regression outweighs whatever 
advantage was gained in looking at the smaller time period. 


V 



1 Summer 3 Winter 

2 Fall 4 Spring 

Season 


c Median 
I I 25%-75% 
~T~ 5%-95% 
c Outliers 


Figure A-1 . Adjusted R 2 values from regressing the outdoor contribution 
to personal exposure on outdoor PM 25 measurements just outside the 
house. 


1.2 

1.0 

0.8 

0.6 

£ 

N 

a. 0.4 

0.2 

0.0 

- 0.2 



1 Summer 3 Winter 


2 Fall 4 Spring 

Season 


c Median 
I I 25%-75% 
~T~ 5%-95% 
c Outliers 


Figure A-2. Adjusted R 2 values from regressing the outdoor contribution 
to personal exposure on outdoor PM 25 Harvard Impactor (HI) 
measurements at the central site. 


64 
































































1.2 


1.0 

0.8 

0.6 



0.2 

0.0 

- 0.2 

■0.4 



1 Summer 3 Winter 

2 Fall 4 Spring 

Season 


o Median 
I 1 25%-75% 
T 5%-95% 
c Outliers 
* Extremes 


Figure A-3. Adjusted R 2 values from regressing the outdoor contribution 
to personal exposure on outdoor PM 2 5 Federal Reference Method (FRM) 
measurements at the central site. 


65 




























Table A-1. Values of the Average Sulfur Indoor/Outdoor Ratio (F inf ) and the Air Exchange Rates by House and by Season 


House 

Season 

N 

S ln/Out a 

SE 

Airex b 

SE 

1 

Summer 

8 

0.58 

0.03 

0.63 

0.10 

1 

Fall 

6 

0.64 

0.03 

0.79 

0.11 

1 

Winter 

2 

0.76 

0.06 

1.29 

0.19 

1 

Spring 

6 

0.65 

0.03 

0.82 

0.11 

2 

Summer 

6 

0.59 

0.03 

0.46 

0.11 

2 

Fall 

4 

0.53 

0.04 

0.81 

0.14 

2 

Winter 

7 

0.64 

0.03 

1.35 

0.10 

2 

Spring 

6 

0.66 

0.03 

0.71 

0.11 

3 

Summer 

8 

0.78 

0.03 

1.05 

0.10 

3 

Fall 

6 

0.70 

0.03 

0.50 

0.11 

3 

Winter 

7 

0.69 

0.03 

0.87 

0.10 

3 

Spring 

7 

0.76 

0.03 

1.07 

0.10 

4 

Summer 

3 

0.32 

0.05 

0.21 

0.16 

4 

Fall 

7 

0.48 

0.03 

0.26 

0.10 

4 

Winter 

4 

0.51 

0.04 

0.34 

0.14 

4 

Spring 

7 

0.45 

0.03 

0.22 

0.10 

5 

Summer 

7 

0.51 

0.03 

0.43 

0.10 

5 

Fall 

6 

0.65 

0.03 

0.36 

0.11 

5 

Spring 

7 

0.74 

0.03 

0.60 

0.10 

6 

Summer 

7 

0.60 

0.03 

0.45 

0.10 

6 

Fall 

5 

0.65 

0.04 

0.57 

0.12 

6 

Winter 

7 

0.58 

0.03 

0.60 

0.10 

6 

Spring 

7 

0.61 

0.03 

0.58 

0.10 

7 

Summer 

7 

0.67 

0.03 

0.56 

0.10 

7 

Fall 

7 

0.83 

0.03 

1.06 

0.10 

7 

Winter 

5 

0.75 

0.04 

1.50 

0.12 

7 

Spring 

6 

0.86 

0.03 

1.94 

0.11 

8 

Summer 

6 

0.36 

0.03 

0.24 

0.11 

9 

Summer 

6 

0.48 

0.03 

0.23 

0.11 

9 

Fall 

5 

0.55 

0.04 

0.40 

0.12 

9 

Winter 

5 

0.62 

0.04 

0.84 

0.12 

9 

Spring 

5 

0.69 

0.04 

0.66 

0.12 

10 

Summer 

6 

0.46 

0.03 

0.30 

0.11 

10 

Fall 

7 

0.57 

0.03 

0.54 

0.10 

10 

Winter 

5 

0.56 

0.04 

0.67 

0.12 

10 

Spring 

7 

0.64 

0.03 

0.66 

0.10 

11 

Summer 

6 

0.69 

0.03 

0.39 

0.11 

11 

Winter 

2 

0.67 

0.06 

4.49 

0.19 

12 

Summer 

7 

0.40 

0.03 

0.27 

0.10 

12 

Fall 

7 

0.57 

0.03 

0.45 

0.10 

12 

Winter 

7 

0.62 

0.03 

0.64 

0.10 

12 

Spring 

7 

0.54 

0.03 

0.33 

0.10 

13 

Summer 

7 

0.37 

0.03 

0.22 

0.10 

14 

Summer 

7 ^ 

0.41 

0.03 

0.23 

0.10 


66 











Table A-1. Continued 


House 

Season 

N 

S ln/Out a 

SE 

Airex b 

SE 

14 

Fall 

6 

0.46 

0.03 

0.33 

0.11 

14 

Winter 

7 

0.45 

0.03 

0.51 

0.10 

14 

Spring 

7 

0.47 

0.03 

0.35 

0.10 

15 

Summer 

7 

0.61 

0.03 

0.42 

0.10 

15 

Fall 

7 

0.73 

0.03 

0.49 

0.10 

15 

Winter 

3 

0.76 

0.05 

0.72 

0.16 

15 

Spring 

7 

0.77 

0.03 

0.56 

0.10 

16 

Summer 

7 

0.59 

0.03 

0.41 

0.10 

16 

Fall 

7 

0.71 

0.03 

0.65 

0.10 

16 

Wnter 

7 

0.68 

0.03 

1.28 

0.10 

16 

Spring 

7 

0.61 

0.03 

0.95 

0.10 

17 

Summer 

7 

0.42 

0.03 

0.11 

0.10 

17 

Fall 

5 

0.53 

0.04 

0.14 

0.12 

17 

Winter 

1 

0.43 

0.08 

0.51 

0.27 

17 

Spring 

6 

0.59 

0.03 

0.27 

0.11 

18 

Summer 

6 

0.52 

0.03 

0.30 

0.11 

19 

Summer 

6 

0.62 

0.03 

0.61 

0.11 

19 

Fall 

7 

0.79 

0.03 

1.33 

0.10 

19 

Wnter 

7 

0.84 

0.03 

1.54 

0.10 

19 

Spring 

7 

0.73 

0.03 

0.93 

0.10 

20 

Summer 

7 

0.56 

0.03 

0.24 

0.10 

20 

Fall 

6 

0.43 

0.03 

0.23 

0.11 

20 

Wnter 

7 

0.47 

0.03 

0.52 

0.10 

20 

Spring 

7 

0.51 

0.03 

0.35 

0.10 

21 

Summer 

7 

0.43 

0.03 

0.28 

0.10 

21 

Fall 

6 

0.68 

0.03 

0.53 

0.11 

21 

Wnter 

6 

0.67 

0.03 

0.63 

0.11 

21 

Spring 

7 

0.67 

0.03 

0.58 

0.10 

22 

Summer 

6 

0.44 

0.03 

0.27 

0.11 

22 

Fall 

7 

0.65 

0.03 

0.48 

0.10 

23 

Summer 

7 

0.43 

0.03 

0.57 

0.10 

23 

Fall 

6 

0.55 

0.03 

1.02 

0.11 

24 

Summer 

7 

0.41 

0.03 

0.33 

0.10 

24 

Fall 

7 

0.55 

0.03 

0.35 

0.10 

24 

Wnter 

7 

0.60 

0.03 

0.51 

0.10 

24 

Spring 

6 

0.64 

0.03 

0.69 

0.11 

25 

Summer 

2 

0.46 

0.06 

0.27 

0.19 

25 

Fall 

6 

0.81 

0.03 

0.67 

0.11 

26 

Summer 

6 

0.61 

0.03 

0.50 

0.11 

26 

Fall 

6 

0.78 

0.03 

0.79 

0.11 

26 

Winter 

5 

0.70 

0.04 

1.15 

0.12 

26 

Spring 

5 

0.73 

0.04 

0.79 

0.12 

27 

Summer 

6 

0.54 

0.03 

0.61 

0.11 

27 

Fall 

7 

0.77 

0.03 

1.25 

0.10 


67 








Table A-1. Continued 


House 

Season 

N 

S ln/Out a 

SE 

Airex b 

SE 

27 

Winter 

7 

0.66 

0.03 

2.61 

0.10 

27 

Spring 

7 

0.68 

0.03 

0.67 

0.10 

28 

Summer 

5 

0.41 

0.04 

0.28 

0.12 

28 

Fall 

7 

0.65 

0.03 

0.47 

0.10 

28 

Winter 

6 

0.61 

0.03 

0.86 

0.11 

28 

Spring 

6 

0.62 

0.03 

0.37 

0.11 

29 

Summer 

13 

0.40 

0.02 

0.31 

0.08 

29 

Fall 

6 

0.59 

0.03 

0.42 

0.11 

29 

Wnter 

5 

0.75 

0.04 

0.61 

0.12 

29 

Spring 

7 

0.70 

0.03 

0.53 

0.10 

31 

Summer 

7 

0.97 

0.03 

2.77 

0.10 

31 

Fall 

7 

0.80 

0.03 

1.11 

0.10 

32 

Summer 

7 

0.25 

0.03 

0.23 

0.10 

32 

Fall 

7 

0.40 

0.03 

0.27 

0.10 

32 

Wnter 

7 

0.48 

0.03 

0.33 

0.10 

32 

Spring 

7 

0.31 

0.03 

0.25 

0.10 

33 

Summer 

5 

0.23 

0.04 

0.15 

0.12 

33 

Fall 

5 

0.35 

0.04 

0.25 

0.12 

33 

Wnter 

4 

0.49 

0.04 

1.12 

0.14 

33 

Spring 

6 

0.40 

0.03 

0.64 

0.11 

34 

Summer 

7 

0.54 

0.03 

0.36 

0.10 

34 

Fall 

3 

0.74 

0.05 

0.45 

0.16 

36 

Summer 

5 

0.25 

0.04 

0.25 

0.12 

37 

Fall 

5 

0.58 

0.04 

0.27 

0.12 

37 

Wnter 

7 

0.66 

0.03 

0.69 

0.10 

37 

Spring 

7 

0.55 

0.03 

0.30 

0.10 

38 

Fall 

3 

0.52 

0.05 

0.18 

0.16 

38 

Wnter 

5 

0.64 

0.04 

0.54 

0.12 

38 

Spring 

7 

0.50 

0.03 

0.26 

0.10 


Mean 

720 

0.59 


0.64 



SD 


0.14 


0.56 



a Average ratio of indoor/outdoor sulfur concentrations 
b Harmonic average of air exchange rate (h' 1 ) 




68 










Table A-2. Comparison of Seasonal Average Sulfur Indoor/Outdoor Ratios (Sin/Sout) with Slopes of Regressions of Sin on Sout 


House 

Season 

N 

Sin/Sout 

.(F'M) 

SE 

Intercept 

SE 

P (Int) 

Slope 

SE 

p (Slope) 

R 2 

(adj.) 

Ratio 3 

1 

S 

8 

0.58 

0.01 

99 

111 

0.41 

0.52 

0.06 

0.0001 

0.92 

0.90 

1 

F 

6 

0.64 

0.03 

96 

101 

0.40 

0.57 

0.06 

0.0008 

0.94 

0.88 

1 

W 

7 

0.66 

0.06 

-64 

210 

0.77 

0.72 

0.15 

0.0045 

0.79 

1.09 

1 

V 

6 

0.65 

0.03 

106 

135 

0.48 

0.57 

0.09 

0.0027 

0.89 

0.87 

2 

s 

6 

0.59 

0.04 

552 

222 

0.07 

0.30 

0.10 

0.0447 

0.60 

0.51 

2 

F 

7 

0.51 

0.02 

18 

153 

0.91 

0.51 

0.05 

0.0001 

0.95 

0.99 

2 

W 

7 

0.64 

0.02 

39 

113 

0.75 

0.60 

0.09 

0.0010 

0.88 

0.94 

2 

V 

6 

0.66 

0.01 

22 

77 

0.79 

0.64 

0.06 

0.0005 

0.95 

0.97 

3 

s 

8 

0.78 

0.03 

537 

193 

0.03 

0.48 

0.10 

0.0027 

0.77 

0.62 

3 

F 

7 

0.71 

0.02 

60 

173 

0.74 

0.68 

0.07 

0.0002 

0.94 

0.96 

3 

w 

7 

0.69 

0.04 

190 

88 

0.08 

0.54 

0.06 

0.0002 

0.94 

0.78 

3 

V 

7 

0.76 

0.02 

365 

119 

0.03 

0.62 

0.04 

0.0001 

0.97 

0.81 

4 

s 

3 

0.32 

0.06 

880 

108 

0.08 

0.00 

0.03 

0.9711 

-1.00 

0.00 

4 

F 

7 

0.48 

0.02 

104 

157 

0.54 

0.42 

0.07 

0.0014 

0.87 

0.88 

4 

W 

6 

0.50 

0.02 

45 

116 

0.72 

0.47 

0.07 

0.0025 

0.90 

0.94 

4 

V 

7 

0.45 

0.06 

166 

87 

0.12 

0.31 

0.04 

0.0004 

0.92 

0.68 

5 

s 

7 

0.51 

0.01 

198 

105 

0.12 

0.44 

0.04 

0.0001 

0.96 

0.85 

5 

F 

6 

0.65 

0.04 

-250 

222 

0.32 

0.83 

0.15 

0.0055 

0.85 

1.29 

5 

W 

7 

0.71 

0.05 

-68 

185 

0.73 

0.77 

0.13 

0.0018 

0.85 

1.09 

5 

V 

7 

0.74 

0.08 

-148 

403 

0.73 

0.81 

0.17 

0.0050 

0.78 

1.10 

6 

S 

7 

0.60 

0.03 

164 

270 

0.57 

0.54 

0.10 

0.0030 

0.82 

0.89 

6 

F 

7 

0.63 

0.02 

-100 

187 

0.62 

0.67 

0.06 

0.0001 

0.95 

1.07 

6 

W 

7 

0.58 

0.01 

-63 

47 

0.24 

0.63 

0.03 

0.0001 

0.99 

1.08 

6 

V 

7 

0.61 

0.01 

52 

66 

0.47 

0.57 

0.04 

0.0001 

0.97 

0.93 

7 

s 

7 

0.67 

0.02 

37 

238 

0.88 

0.66 

0.07 

0.0002 

0.95 

0.98 

7 

F 

7 

0.83 

0.02 

-288 

54 

0.00 

0.97 

0.02 

0.0001 

1.00 

1.17 

7 

W 

7 

0.76 

0.02 

-3 

53 

0.96 

0.76 

0.05 

0.0001 

0.97 

1.00 

7 

V 

7 

0.84 

0.03 

-257 

119 

0.08 

1.07 

0.10 

0.0001 

0.95 

1.28 

8 

s 

6 

0.36 

0.02 

291 

247 

0.31 

0.27 

0.06 

0.0141 

0.77 

0.75 

9 

s 

6 

0.48 

0.02 

-14 

227 

0.95 

0.49 

0.07 

0.0017 

0.92 

1.01 

9 

F 

6 

0.57 

0.05 

284 

223 

0.27 

0.34 

0.17 

0.1111 

0.39 

0.59 

9 

w 

7 

0.61 

0.01 

23 

26 

0.40 

0.58 

0.03 

0.0001 

0.99 

0.95 

9 

V 

5 

0.69 

0.10 

263 

477 

0.62 

0.53 

0.16 

0.0484 

0.70 

0.77 

10 

s 

6 

0.46 

0.01 

76 

164 

0.67 

0.43 

0.06 

0.0014 

0.92 

0.94 

10 

F 

7 

0.57 

0.03 

45 

94 

0.66 

0.54 

0.06 

0.0003 

0.93 

0.94 

10 

W 

7 

0.60 

0.04 

192 

119 

0.17 

0.37 

0.13 

0.0389 

0.53 

0.61 

10 

V 

7 

0.64 

0.06 

1002 

266 

0.01 

0.22 

0.09 

0.0518 

0.48 

0.35 

11 

s 

6 

0.69 

0.03 

382 

368 

0.36 

0.55 

0.12 

0.0098 

0.80 

0.80 

11 

w 

2 

0.67 

0.01 









12 

s 

7 

0.40 

0.02 

253 

184 

0.23 

0.31 

0.06 

0.0033 

0.82 

0.78 

12 

F 

7 

0.57 

0.02 

69 

90 

0.47 

0.51 

0.05 

0.0002 

0.94 

0.89 

12 

W 

7 

0.62 

0.03 

23 

57 

0.70 

0.60 

0.04 

0.0001 

0.97 

0.96 

12 

V 

7 

0.54 

0.06 

132 

100 

0.24 

0.41 

0.04 

0.0002 

0.94 

0.76 

13 

S 

7 

0.37 

0.03 

60 

78 

0.48 

0.32 

0.05 

0.0010 

0.88 

0.87 

14 

S 

7 

0.41 

0.04 

155 

113 

0.23 

0.28 

0.07 

0.0111 

0.71 

0.69 


69 
















Table A-2. Continued 


House 

Season 

N 

Sin/Sout 

(Flnf) 

SE 

Intercept 

SE 

P (Int) 

Slope 

SE 

p (Slope) 

R 

(adj.) 

Ratio 1 

14 

F 

6 

0.46 

0.04 

54 

111 

0.65 

0.42 

0.07 

0.0040 

0.87 

0.91 

14 

W 

7 

0.45 

0.04 

-71 

139 

0.63 

0.53 

0.12 

0.0062 

0.77 

1.17 

14 

V 

7 

0.47 

0.02 

316 

164 

0.11 

0.33 

0.07 

0.0050 

0.78 

0.69 

15 

S 

7 

0.61 

0.04 

85 

116 

0.50 

0.55 

0.07 

0.0007 

0.90 

0.89 

15 

F 

7 

0.73 

0.02 

-15 

60 

0.82 

0.75 

0.04 

0.0001 

0.98 

1.02 

15 

W 

6 

0.66 

0.05 

-212 

145 

0.22 

0.91 

0.15 

0.0033 

0.88 

1.38 

15 

V 

7 

0.77 

0.03 

-116 

147 

0.34 

0.88 

0.09 

0.0002 

0.93 

1.15 

16 

S 

7 

0.59 

0.02 

246 

126 

0.11 

0.50 

0.04 

0.0001 

0.96 

0.83 

16 

F 

7 

0.71 

0.03 

-49 

106 

0.67 

0.45 

0.07 

0.0001 

0.96 

0.63 

16 

W 

7 

0.68 

0.01 

34 

15 

0.08 

0.64 

0.01 

0.0001 

1.00 

0.94 

16 

V 

7 

0.61 

0.02 

-217 

77 

0.04 

0.81 

0.06 

0.0001 

0.96 

1.31 

17 

s 

7 

0.42 

0.03 

4 

340 

0.99 

0.42 

0.12 

0.0190 

0.64 

1.00 

17 

F 

6 

0.52 

0.05 

285 

198 

0.22 

0.35 

0.07 

0.0090 

0.81 

0.67 

17 

W 

7 

0.55 

0.06 

-31 

222 

0.89 

0.59 

0.19 

0.0250 

0.60 

1.07 

17 

V 

6 

0.59 

0.03 

102 

87 

0.31 

0.51 

0.05 

0.0006 

0.95 

0.88 

18 

S 

6 

0.52 

0.03 

226 

173 

0.26 

0.42 

0.06 

0.0025 

0.90 

0.80 

19 

S 

6 

0.62 

0.03 

-118 

510 

0.83 

0.65 

0.14 

0.0086 

0.82 

1.06 

19 

F 

7 

0.79 

0.01 

-19 

31 

0.57 

0.80 

0.02 

0.0001 

1.00 

1.02 

19 

W 

7 

0.84 

0.05 

135 

115 

0.29 

0.69 

0.10 

0.0009 

0.89 

0.82 

19 

V 

7 

0.73 

0.02 

-102 

504 

0.85 

0.77 

0.18 

0.0084 

0.74 

1.05 

20 

s 

7 

0.56 

0.04 

755 

197 

0.01 

0.32 

0.05 

0.0016 

0.86 

0.58 

20 

F 

6 

0.43 

0.02 

-25 

108 

0.82 

0.45 

0.06 

0.0017 

0.92 

1.04 

20 

W 

7 

0.47 

0.02 

-33 

59 

0.60 

0.50 

0.05 

0.0001 

0.95 

1.07 

20 

V 

7 

0.51 

0.01 

-152 

192 

0.46 

0.57 

0.07 

0.0006 

0.91 

1.12 

21 

S 

7 

0.43 

0.02 

-151 

167 

0.41 

0.48 

0.05 

0.0001 

0.95 

1.11 

21 

F 

7 

0.70 

0.03 

-73 

312 

0.82 

0.73 

0.11 

0.0012 

0.88 

1.04 

21 

W 

6 

0.67 

0.03 

173 

66 

0.06 

0.50 

0.05 

0.0006 

0.95 

0.75 

21 

V 

7 

0.67 

0.02 

-55 

91 

0.58 

0.72 

0.08 

0.0003 

0.93 

1.08 

22 

S 

6 

0.44 

0.03 

586 

177 

0.03 

0.22 

0.06 

0.0247 

0.69 

0.49 

22 

F 

7 

0.65 

0.03 

89 

128 

0.52 

0.60 

0.06 

0.0001 

0.95 

0.92 

23 

S 

7 

0.43 

0.02 

88 

168 

0.62 

0.40 

0.04 

0.0003 

0.93 

0.93 

23 

F 

6 

0.55 

0.02 

15 

63 

0.82 

0.54 

0.04 

0.0002 

0.97 

0.97 

24 

S 

7 

0.41 

0.02 

248 

174 

0.21 

0.32 

0.06 

0.0035 

0.81 

0.77 

24 

F 

7 

0.55 

0.03 

125 

127 

0.37 

0.48 

0.05 

0.0002 

0.94 

0.86 

24 

W 

7 

0.60 

0.04 

67 

66 

0.35 

0.51 

0.05 

0.0002 

0.94 

0.86 

24 

V 

6 

0.64 

0.04 

-129 

218 

0.59 

0.76 

0.19 

0.0150 

0.76 

1.20 

25 

S 

2 

0.46 

0.01 









25 

F 

6 

0.81 

0.02 

-95 

173 

0.62 

0.86 

0.08 

0.0003 

0.96 

1.06 

26 

S 

6 

0.61 

0.01 

-66 

212 

0.77 

0.63 

0.05 

0.0002 

0.97 

1.03 

26 

F 

6 

0.78 

0.02 

-63 

349 

0.87 

0.80 

0.11 

0.0016 

0.92 

1.03 

26 

W 

7 

0.69 

0.02 

100 

87 

0.30 

0.57 

0.10 

0.0020 

0.85 

0.83 

26 

V 

5 

0.73 

0.04 

-680 

1169 

0.60 

0.99 

0.43 

0.1038 

0.52 

1.34 

27 

S 

7 

0.54 

0.01 

-81 

73 

0.32 

0.57 

0.02 

0.0001 

0.99 

1.05 

27 

F 

7 

0.77 

0.03 

-2 

126 

0.99 

0.77 

0.08 

0.0002 

0.94 

1.00 

27 

W 

7 

0.66 

0.01 

-3 

50 

0.96 

0.66 

0.04 

0.0001 

0.98 

1.00 


70 














Table A-2. Continued 


House 

Season 

N 

Sin/Sout 

.(Finf). 

SE 

Intercept 

SE 

P (Int) 

Slope 

SE 

p (Slope) 

(adj.) 

Ratio 3 

27 

V 

7 

0.68 

0.04 

257 

273 

0.39 

0.56 

0.11 

0.0040 

0.80 

0.82 

28 

S 

5 

0.41 

0.02 

126 

241 

0.64 

0.37 

0.07 

0.0210 

0.88 

0.89 

28 

F 

7 

0.65 

0.01 

-20 

25 

0.46 

0.67 

0.01 

0.0001 

1.00 

1.03 

28 

W 

7 

0.62 

0.02 

173 

68 

0.05 

0.42 

0.07 

0.0024 

0.84 

0.67 

28 

V 

6 

0.62 

0.05 

980 

319 

0.04 

0.14 

0.15 

0.4100 

-0.03 

0.22 

29 

s 

13 

0.40 

0.02 

89 

210 

0.68 

0.36 

0.06 

0.0001 

0.73 

0.90 

29 

F 

7 

0.61 

0.05 

182 

311 

0.58 

0.47 

0.22 

0.0840 

0.38 

0.77 

29 

W 

7 

0.71 

0.06 

259 

142 

0.13 

0.39 

0.15 

0.0508 

0.48 

0.55 

29 

V 

7 

0.70 

0.03 

135 

167 

0.46 

0.60 

0.10 

0.0019 

0.85 

0.86 

31 

s 

7 

0.97 

0.02 

59 

106 

0.60 

0.92 

0.07 

0.0001 

0.97 

0.95 

31 

F 

7 

0.80 

0.03 

-319 

160 

0.10 

0.95 

0.06 

0.0001 

0.97 

1.19 

32 

S 

7 

0.25 

0.02 

2 

71 

0.97 

0.25 

0.05 

0.0032 

0.82 

0.98 

32 

F 

7 

0.40 

0.02 

35 

91 

0.72 

0.38 

0.05 

0.0005 

0.91 

0.94 

32 

W 

7 

0.48 

0.03 

-6 

112 

0.96 

0.48 

0.07 

0.0011 

0.88 

1.01 

32 

V 

7 

0.31 

0.02 

335 

129 

0.05 

0.16 

0.05 

0.0306 

0.57 

0.52 

33 

S 

5 

0.23 

0.04 

95 

31 

0.06 

0.12 

0.02 

0.0078 

0.91 

0.55 

33 

F 

6 

0.40 

0.07 

454 

373 

0.29 

0.05 

0.27 

0.8700 

-0.24 

0.12 

33 

W 

5 

0.49 

0.05 

-89 

146 

0.58 

0.60 

0.17 

0.0350 

0.76 

1.23 

33 

V 

7 

0.42 

0.04 

165 

83 

0.10 

0.29 

0.03 

0.0003 

0.93 

0.69 

34 

s 

7 

0.54 

0.03 

-41 

174 

0.83 

0.56 

0.06 

0.0002 

0.94 

1.03 

34 

F 

5 

0.72 

0.03 

-21 

331 

0.95 

0.74 

0.11 

0.0060 

0.92 

1.02 

36 

S 

6 

0.26 

0.02 

10 

93 

0.92 

0.26 

0.03 

0.0017 

0.92 

0.99 

37 

F 

6 

0.59 

0.04 

-152 

326 

0.66 

0.69 

0.21 

0.0290 

0.67 

1.17 

37 

W 

7 

0.66 

0.03 

-94 

129 

0.50 

0.73 

0.08 

0.0003 

0.93 

1.10 

37 

V 

7 

0.55 

0.03 

565 

113 

0.00 

0.30 

0.04 

0.0011 

0.88 

0.54 

38 

F 

3 

0.52 

0.03 

-23 

87 

0.84 

0.54 

0.07 

0.0869 

0.96 

1.05 

38 

W 

7 

0.64 

0.03 

136 

112 

0.28 

0.46 

0.13 

0.0172 

0.65 

0.73 

38 

V 

7 

0.50 

0.01 

96 

59 

0.17 

0.41 

0.05 

0.0006 

0.90 

0.81 

Mean 


775 

0.59 

0.03 

89 

167 

0.47 

0.54 

0.08 

0.03 

0.83 

0.91 

SD 



0.14 

0.02 

241 

137 

0.30 

0.20 

0.06 

0.13 

0.25 

0.26 


a Ratio of slope to Sin/Sout. 


71 











Table A-3. Comparison of Seasonal Average Sulfur Personal/Outdoor Ratios (Spers/Sout) with Slopes of Regressions of Spers on Sout 


Subject 

Season 

N 

Spers/Sout 

(Fpex) 

SE 

Slope 

SE 

P 

Intercept 

SE 

P 

R 2 (adj) 

Ratio 3 

1 

S 

6 

0.50 

0.02 

0.52 

0.09 

0.0049 

-53 

184 

0.79 

0.86 

1.06 

1 

F 

6 

0.58 

0.03 

0.57 

0.05 

0.0003 

17 

80 

0.84 

0.96 

0.98 

1 

W 

7 

0.59 

0.05 

0.66 

0.14 

0.0051 

-76 

197 

0.71 

0.78 

1.11 

1 

V 

6 

0.65 

0.05 

0.54 

0.11 

0.0078 

141 

169 

0.45 

0.82 

0.83 

2 

s 

6 

0.47 

0.04 

0.29 

0.12 

0.0683 

345 

254 

0.25 

0.51 

0.62 

2 

F 

6 

0.48 

0.03 

0.46 

0.09 

0.0073 

55 

269 

0.85 

0.83 

0.96 

2 

w 

6 

0.47 

0.04 

0.56 

0.16 

0.0240 

-103 

216 

0.66 

0.70 

1.18 

2 

V 

6 

0.59 

0.02 

0.74 

0.09 

0.0012 

-173 

110 

0.19 

0.93 

1.26 

3 

s 

5 

0.72 

0.04 

0.45 

0.44 

0.3799 

439 

736 

0.59 

0.01 

0.63 

3 

F 

7 

0.64 

0.04 

0.68 

0.12 

0.0026 

-95 

304 

0.77 

0.83 

1.06 

3 

w 

7 

0.60 

0.02 

0.50 

0.03 

0.0001 

122 

53 

0.07 

0.97 

0.84 

3 

V 

2 

0.59 

0.01 









4 

s 

2 

0.41 

0.01 









4 

F 

7 

0.48 

0.04 

0.33 

0.09 

0.0150 

282 

218 

0.25 

0.67 

0.69 

4 

W 

7 

0.50 

0.05 

0.35 

0.13 

0.0440 

182 

206 

0.42 

0.51 

0.70 

4 

V 

7 

0.47 

0.06 

0.55 

0.12 

0.0051 

-131 

282 

0.66 

0.78 

1.17 

5 

S 

6 

0.59 

0.04 

0.46 

0.10 

0.0085 

307 

279 

0.33 

0.82 

0.78 

5 

F 

7 

0.65 

0.05 

0.66 

0.23 

0.0336 

0 

315 

1.00 

0.55 

1.00 

5 

W 

7 

0.66 

0.03 

0.62 

0.07 

0.0004 

61 

105 

0.59 

0.92 

0.93 

5 

V 

7 

0.82 

0.04 

0.79 

0.06 

0.0001 

12 

152 

0.94 

0.96 

0.97 

6 

s 

6 

0.44 

0.04 

0.42 

0.13 

0.0327 

21 

369 

0.96 

0.65 

0.96 

6 

F 

7 

0.55 

0.03 

0.68 

0.08 

0.0004 

-341 

254 

0.24 

0.92 

1.24 

6 

W 

7 

0.50 

0.02 

0.55 

0.04 

0.0001 

-76 

63 

0.28 

0.97 

1.12 

6 

V 

7 

0.54 

0.02 

0.45 

0.05 

0.0003 

109 

79 

0.23 

0.93 

0.85 

7 

S 

6 

0.54 

0.02 

0.50 

0.11 

0.0111 

131 

429 

0.77 

0.79 

0.93 

7 

F 

7 

0.66 

0.05 

0.99 

0.06 

0.0001 

-680 

150 

0.01 

0.98 

1.51 

7 

W 

7 

0.56 

0.04 

0.78 

0.12 

0.0013 

-180 

119 

0.19 

0.87 

1.38 

7 

V 

7 

0.70 

0.04 

1.00 

0.11 

0.0003 

-334 

137 

0.06 

0.93 

1.44 

8 

S 

6 

0.36 

0.01 

0.33 

0.04 

0.0007 

77 

138 

0.61 

0.94 

0.93 

9 

S 

6 

0.47 

0.02 

0.43 

0.06 

0.0016 

120 

198 

0.58 

0.92 

0.92 

9 

F 

7 

0.58 

0.07 

0.56 

0.30 

0.1230 

31 

400 

0.94 

0.29 

0.96 

9 

W 

7 

0.57 

0.07 

0.46 

0.20 

0.0660 

100 

192 

0.62 

0.43 

0.80 

9 

V 

5 

0.70 

0.05 

0.72 

0.14 

0.0128 

-54 

393 

0.90 

0.87 

1.03 

10 

s 

5 

0.44 

0.02 

0.48 

0.05 

0.0031 

-91 

161 

0.61 

0.95 

1.08 

10 

F 

7 

0.46 

0.03 

0.36 

0.05 

0.0008 

104 

76 

0.23 

0.90 

0.79 

10 

w 

7 

0.51 

0.03 

0.48 

0.08 

0.0023 

17 

76 

0.83 

0.84 

0.95 

10 

V 

2 

0.62 

0.25 









11 

s 

6 

0.76 

0.04 

0.49 

0.12 

0.0148 

746 

370 

0.11 

0.76 

0.65 

11 

w 

3 

0.62 

0.05 

0.77 

0.13 

0.1075 

-118 

127 

0.52 

0.94 

1.23 

12 

s 

7 

0.40 

0.02 

0.28 

0.04 

0.0015 

337 

135 

0.05 

0.87 

0.69 

12 

F 

7 

0.51 

0.02 

0.45 

0.06 

0.0009 

72 

105 

0.52 

0.89 

0.87 

12 

W 

7 

0.56 

0.06 

0.60 

0.09 

0.0012 

-56 

129 

0.68 

0.88 

1.07 

12 

V 

6 

0.44 

0.03 

0.42 

0.05 

0.0008 

10 

121 

0.94 

0.94 

0.95 

13 

s 

7 

0.43 

0.04 

0.39 

0.10 

0.0094 

37 

159 

0.82 

0.72 

0.92 


72 













7 

7 

6 

7 

6 

7 

7 

7 

6 

7 

7 

7 

6 

6 

7 

6 

6 

6 

7 

7 

2 

7 

6 

7 

2 

7 

7 

7 

7 

6 

7 

7 

7 

7 

6 

6 

4 

7 

7 

6 

6 

7 

2 

7 


Spers/Sout 

-J&d . 

SE 

Slope 

SE 

P 

Intercept 

SE 

P 

R 2 (adj) 

Ratio' 

0.40 

0.02 

0.35 

0.05 

0.0011 

62 

83 

0.49 

0.88 

0.87 

0.43 

0.02 

0.51 

0.05 

0.0001 

-99 

68 

0.21 

0.96 

1.21 

0.43 

0.04 

0.65 

0.14 

0.0097 

-247 

178 

0.24 

0.80 

1.51 

0.48 

0.03 

0.23 

0.05 

00084 

526 

132 

0.01 

0.74 

0.49 

0.59 

0.03 

0.69 

0.08 

0.0011 

-129 

137 

0.40 

0.93 

1.16 

0.61 

0.02 

0.67 

0.03 

0.0001 

-56 

45 

0.27 

0.99 

1.09 

0.61 

0.05 

0.86 

0.14 

0.0019 

-219 

146 

0.19 

0.85 

1.41 

0.64 

0.03 

0.81 

0.05 

0.0001 

-224 

82 

0.04 

0.97 

1.26 

0.57 

0.01 

0.53 

0.06 

0.0008 

107 

177 

0.58 

0.94 

0.93 

0.66 

0.03 

0.66 

0.06 

0.0001 

-16 

100 

0.88 

0.95 

1.01 

0.57 

0.03 

0.46 

0.04 

0.0001 

107 

47 

0.07 

0.96 

0.80 

0.56 

0.03 

0.90 

0.05 

0.0001 

-389 

59 

0.00 

0.98 

1.62 

0.44 

0.03 

0.51 

0.09 

0.0053 

-176 

271 

0.55 

0.85 

1.17 

0.43 

0.06 

0.27 

0.08 

0.0293 

250 

216 

0.31 

0.67 

0.61 

0.46 

0.06 

0.47 

0.19 

0.0574 

6 

228 

0.98 

0.46 

1.01 

0.57 

0.03 

0.55 

0.08 

0.0023 

34 

134 

0.81 

0.90 

0.95 

0.51 

0.02 

0.47 

0.07 

0.0028 

76 

201 

0.73 

0.89 

0.93 

0.59 

0.03 

0.61 

0.11 

0.0055 

-49 

417 

0.91 

0.85 

1.03 

0.72 

0.01 

0.68 

0.02 

0.0001 

45 

41 

0.32 

0.99 

0.95 

0.72 

0.03 

0.66 

0.07 

0.0002 

49 

82 

0.57 

0.94 

0.92 

0.66 

0.06 









0.55 

0.03 

0.42 

0.07 

0.0018 

412 

265 

0.18 

0.85 

0.77 

0.45 

0.03 

0.39 

0.09 

0.0103 

69 

156 

0.68 

0.80 

0.88 

0.39 

0.02 

0.50 

0.04 

0.0001 

-102 

44 

0.07 

0.97 

1.26 

0.52 

0.03 









0.55 

0.03 

0.54 

0.07 

0.0007 

33 

265 

0.91 

0.90 

0.98 

0.74 

0.01 

0.71 

0.03 

0.0001 

81 

97 

0.44 

0.99 

0.95 

0.75 

0.05 

0.52 

0.08 

0.0015 

197 

98 

0.10 

0.87 

0.70 

0.73 

0.05 

1.21 

0.15 

0.0004 

-505 

167 

0.03 

0.92 

1.66 

0.48 

0.04 

0.22 

0.11 

0.1198 

660 

323 

0.11 

0.37 

0.47 

0.60 

0.03 

0.68 

0.05 

0.0001 

-92 

111 

0.45 

0.97 

1.12 

0.47 

0.03 

0.49 

0.13 

0.0120 

-66 

473 

0.89 

0.70 

1.04 

0.50 

0.02 

0.46 

0.04 

0.0001 

39 

62 

0.56 

0.95 

0.92 

0.53 

0.02 

0.64 

0.04 

0.0001 

-275 

114 

0.06 

0.98 

1.20 

0.53 

0.04 

0.48 

0.04 

0.0002 

71 

99 

0.52 

0.97 

0.90 

0.63 

0.10 

0.49 

0.16 

0.0377 

125 

215 

0.59 

0.63 

0.79 

0.64 

0.03 

0.67 

0.16 

0.0547 

-32 

193 

0.88 

0.84 

1.05 

0.52 

0.02 

0.49 

0.07 

0.0012 

68 

278 

0.82 

0.88 

0.95 

0.70 

0.02 

0.86 

0.06 

0.0001 

-317 

141 

0.07 

0.97 

1.23 

0.52 

0.02 

0.47 

0.05 

0.0006 

187 

199 

0.40 

0.95 

0.89 

0.70 

0.01 

0.65 

0.05 

0.0002 

136 

157 

0.43 

0.97 

0.93 

0.56 

0.02 

0.42 

0.09 

0.0051 

120 

79 

0.19 

0.78 

0.75 

0.74 

0.02 









0.46 

0.01 

0.43 

0.03 

0.0001 

90 

105 

0.43 

0.97 

0.93 


73 

















Table A-3. Continued 


Subject 

Season 

N 

Spers/Sout 

(Fpex) 

SE 

Slope 

SE 

P 

Intercept 

SE 

P 

R 2 (adj) 

Ratio 3 

27 

F 

7 

0.62 

0.01 

0.60 

0.04 

0.0001 

20 

65 

0.77 

0.97 

0.97 

27 

W 

7 

0.58 

0.02 

0.54 

0.06 

0.0004 

49 

75 

0.54 

0.92 

0.92 

27 

V 

7 

0.54 

0.03 

0.50 

0.10 

0.0034 

98 

235 

0.69 

0.81 

0.92 

28 

S 

5 

0.39 

0.02 

0.36 

0.06 

0.0089 

109 

211 

0.64 

0.90 

0.92 

28 

F 

7 

0.50 

0.02 

0.54 

0.06 

0.0002 

-61 

108 

0.60 

0.94 

1.10 

28 

W 

7 

0.50 

0.02 

0.33 

0.08 

0.0085 

144 

72 

0.10 

0.73 

0.66 

28 

V 

6 

0.48 

0.04 

0.10 

0.13 

0.4929 

785 

282 

0.05 

-0.09 

0.20 

29 

s 

7 

0.43 

0.03 

0.49 

0.12 

0.0090 

-156 

428 

0.73 

0.73 

1.12 

29 

F 

6 

0.61 

0.06 

0.97 

0.37 

0.0571 

-503 

541 

0.40 

0.55 

1.58 

29 

W 

7 

0.59 

0.05 

0.28 

0.11 

0.0522 

252 

100 

0.05 

0.47 

0.47 

29 

V 

7 

0.62 

0.06 

0.72 

0.20 

0.0152 

-150 

325 

0.66 

0.67 

1.17 

31 

S 

7 

0.91 

0.02 

0.94 

0.06 

0.0001 

-49 

87 

0.60 

0.98 

1.04 

31 

F 

7 

0.66 

0.03 

0.76 

0.07 

0.0001 

-207 

183 

0.31 

0.95 

1.15 

32 

S 

7 

0.47 

0.03 

0.56 

0.06 

0.0003 

-110 

98 

0.31 

0.93 

1.20 

32 

F 

7 

0.53 

0.04 

0.47 

0.09 

0.0033 

74 

170 

0.68 

0.82 

0.88 

32 

W 

7 

0.60 

0.05 

0.38 

0.09 

0.0099 

267 

145 

0.13 

0.72 

0.63 

32 

V 

7 

0.38 

0.04 

0.15 

0.19 

0.4704 

516 

466 

0.32 

-0.07 

0.39 

33 

s 

4 

0.30 

0.08 

0.16 

0.06 

0.1091 

117 

86 

0.31 

0.69 

0.53 

33 

F 

6 

0.40 

0.07 

0.15 

0.30 

0.6425 

318 

416 

0.49 

-0.18 

0.38 

33 

w 

5 

0.48 

0.04 

0.51 

0.12 

0.0233 

-35 

105 

0.76 

0.81 

1.08 

33 

V 

7 

0.32 

0.03 

0.26 

0.01 

0.0001 

81 

37 

0.08 

0.98 

0.80 

34 

s 

7 

0.52 

0.02 

0.54 

0.04 

0.0001 

-38 

130 

0.78 

0.96 

1.03 

34 

F 

6 

0.74 

0.04 

0.72 

0.14 

0.0064 

85 

417 

0.85 

0.84 

0.97 

35 

s 

7 

0.41 

0.04 

0.28 

0.07 

0.0122 

313 

219 

0.21 

0.69 

0.67 

36 

s 

6 

0.45 

0.03 

0.51 

0.09 

0.0057 

-133 

256 

0.63 

0.85 

1.13 

37 

F 

7 

0.55 

0.09 

0.26 

0.33 

0.4687 

381 

484 

0.47 

-0.07 

0.46 

37 

W 

7 

0.54 

0.02 

0.60 

0.10 

0.0016 

-83 

151 

0.61 

0.86 

1.12 

37 

V 

7 

0.53 

0.03 

0.32 

0.07 

0.0058 

473 

174 

0.04 

0.77 

0.60 

38 

F 

7 

0.54 

0.04 

0.57 

0.06 

0.0002 

-39 

83 

0.66 

0.94 

1.06 

38 

W 

7 

0.57 

0.04 

0.47 

0.19 

0.0566 

79 

160 

0.64 

0.46 

0.82 

38 

V 

7 

0.52 

0.04 

0.36 

0.18 

0.0934 

157 

193 

0.45 

0.35 

0.70 

Mean 


750 

0.55 

0.04 

0.53 

0.10 

0.03 

41 

190 

0.48 

0.79 

0.96 

SD 



0.11 

0.03 

0.19 

0.07 

0.10 

231 

125 

0.29 

0.24 

0.22 


3 Ratio of slope to Spers/Sout. 


V 


74 













Table A-4. Questionnaire Variables and Definitions 


# 

Variable Name 

Definition 

Units 

1 

Persdate 

Julian date*100+Subject ID 


2 

DatelD 

Date (YYYYMMDD)*100 + SUBJECT id 


3 

EPA ID 

epa id 


4 

Subject 

SUBJECT ID 


5 

Cohort 

Cohort (Cardiovascular or hypertensive) 


6 

House 

House ID 


7 

Season 

Season 


8 

Date 

Julian date 


9 

Month 

Month 


10 

Day 

Day 


11 

Year 

Year 


12 

ambHI25 

Central-site Harvard Impactor PM 25 

pg/m 3 

13 

FRM25 

Central-site Federal Reference Method PM 25 

pg/m 3 

14 

Flaghi25 

Validity code: 2 = valid; used when central-site Hl 25 used in regressions 


15 

FlagFRM 

Validity code: 2 = valid; used when FRM is a variable in regressions 


16 

Flagairex 

Validity code: 2 = valid; used when airex is a variable in regressions 


17 

persS 

personal sulfur 

ng/m 3 

18 

Sin 

Indoor sulfur 

ng/m 3 

19 

Sout 

Outdoor sulfur 

ng/m 3 

20 

Sinout 

Indoor/outdoor sulfur ratio 


21 

FlagSinout 

Validity code: 2 = valid; used for all indoor/outdoor regressions 


22 

SpersSout 

personal/outdoor sulfur ratio 


23 

Flagspersout 

Validity code: 2 = valid; used for all personal/outdoor regressions 


24 

PM25in 

Indoor PM 2 . 5 

pg/m 3 

25 

PM25out 

Outdoor PM 2 5 

pg/m 3 

26 

PM25pers 

Personal PM 25 

pg/m 3 

27 

Outcontin 

Outdoor contribution to indoor PM 2 5 concentrations 

pg/m 3 

28 

Outcontpers 

Outdoor contribution to personal PM 25 concentrations 

pg/m 3 

29 

Incontrib 

Indoor-generated contribution to indoor PM 2 5 concentrations 

pg/m 3 

30 

Perscontrib 

Non-outdoor contribution to personal PM 25 concentrations 

pg/m 3 

31 

Perscloud 

Remainder after accounting for time-weighted indoor-outdoor exposures pg/m 3 

32 

airex 

air exchange rate 

h' 1 

33 

Cleaning 

Time spent cleaning 

minutes 

34 

Grooming 

Time spent grooming 

minutes 

35 

Otherjndoor 

Time in other indoor locations 

minutes 

36 

Otherjoc 

Time in other locations 

minutes 

37 

Travel 

Time spent in travel 

minutes 

38 

Unknown 

Time in Unknown location 

minutes 

39 

cooking 

Time spent cooking 

minutes 

40 

outdoor 

Time spent outdoors 

minutes 

41 

smoke 

Time spent near smokers 

minutes 

42 

TempC 

Outdoor Temperature (“C^ 

°C 


75 














Table A-4. Continued 


# 

Variable Name 

Definition 

Units 

43 

Tempdelta 

Absolute Outdoor-Indoor Temp Difference using thermostat setting 

°F 

44 

Numpeopl 

Number of persons living in house 


45 

numsmok 

Number of smokers in house 


46 

cigsmokd 

Number of cigarettes smoked in house 


47 

Mealsckd 

Number of meals cooked 


48 

Burning 

Was food burned today? 


49 

Exhstfan 

Was exhaust fan used today? 


50 

Candles 

Were candles used today? 


51 

CANDLDUR 

Duration of candle use 

minutes 

52 

Incense 

Was incense used today? 


53 

INCENDUR 

Duration of incense use 

minutes 

54 

Windopen 

Were windows open today? 


55 

windowall 

Sum of products of open windows X width opened X duration open 

inch-hours 

56 

spacehtr 

Was a space heater used today? 


57 

Cleaning_1 

Did cleaning occur today? 


58 

Pets 

Any dog or cat pets? 


59 

broil 

broiled food today 


60 

fry 

fried food today 


61 

grill 

grilled food today 


62 

sautee 

sauteed food today 


63 

sweep 

swept floors today 


64 

vacuum 

vacuumed today 


65 

dust 

dusted today 


66 

TYPE 

Type of building (1 = detached, 2 = duplex, 4 = apartment, 6 = trailer) 


67 

AGE 

age of building (years) 

years 

68 

BUSY_RD 

high-traffic road nearby 


69 

DIRT_RD 

dir road nearby 


70 

DUSTY_RD 

Dust from nearby construction etc 


71 

GAR_USE 

Park cars in attached garage? 


72 

A_C 

air conditioning unit(s) in home 


73 

FAN 

whole-house or attic fan 


74 

S_WIN 

Storm windows (0 = none, 0.5 = partial, 1 = all) 


75 

C_FUEL 

cooking fuel (1 = gas, 2 = electricity) 


76 

C_FAN 

range hood? 


77 

PILOT 

Number of pilot lights (0-3) 


78 

DRYER 

clothes dryer? 


79 

DRY_VENT 

clothes dryer vented outside? 


80 

VAC 

vacuum bag type (0 = none, 1 = standard, 2 = HEPA) 


81 

AREA 

area of house (square footage) 

feet 2 

82 

Rooms 

Number of rooms 


83 

FLRCOVav 

Percent of floor covered by carpet 

% 

84 

MILDEWavg 

mildew noticed by technician 


85 

DSTFACavg 

Estimate of dusty/dirty condition by technician (0 = clean, 6 dusty) 



\ 


76 

























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